Since all information signals and noise are random and can be predicted only with a certain degree of probability, probability theory is used to describe such signals. In this case, statistical characteristics are used, which are obtained by conducting numerous experiments under the same conditions.
All random phenomena studied by probability theory can be divided into three groups:
— random events;
— random variables;
- random processes.
Random event is any fact that may or may not happen as a result of experience.
A random event is the appearance of interference at the receiver input or the reception of a message with an error.
Random events are designated by the Latin letters A, B, C.
The numerical characteristics of a random event are:
1. Frequency of occurrence of a random event:
where m is the number of experiments in which this event occurred;
N is the total number of experiments performed.
As follows from expression (40), the frequency of occurrence of a random event cannot exceed 1, since the number of experiments in which this event occurred cannot exceed the total number of experiments performed.
2. Probability of a random event occurring:
That is, the probability of a random event occurring is the frequency of its occurrence with an unlimited increase in the number of experiments performed. The probability of an event occurring cannot exceed 1. A random event with a probability equal to one is reliable, i.e. it will definitely happen, therefore events that have already occurred have such a probability.
Random value is a quantity that changes randomly from experiment to experiment.
A random variable is the amplitude of the interference at the receiver input or the number of errors in the received message. Random variables are denoted by the Latin letters X, Y, Z, and their values are x, y, z.
Random variables can be discrete or continuous.
Discrete is a random variable that can take a finite set of values (for example, the number of equipment, the number of telegrams, etc., since they can only take the integer 1, 2, 3, ...).
Continuous is a random variable that can take any values from a certain range (for example, the amplitude of interference at the receiver input can take any values, just like an analog information signal can take any values).
Numerical, statistical characteristics describing random variables are:
1.Probability distribution function.
F(x)=P(X ? x) (42)
This function shows the probability that the random variable X will not exceed a specifically selected value x. If the random variable X is discrete, then F(x) is also a discrete function, if X is a continuous variable, then F(x) ? continuous function.
2. Probability density function.
P(x)=dF(x)/dx (43)
This characteristic shows the probability of the value of a random variable falling into a small interval dx in the vicinity of point x’, i.e., in the shaded area (figure).
3. Expected value.
where xi are the values of the random variable;
P(xi) is the probability of occurrence of these values;
n is the number of possible values of the random variable.
where p(x) is the probability density of a continuous random variable.
In its meaning, the mathematical expectation shows the average and most probable value of a random variable, i.e. this value is most often taken by a random variable. Expression (44) is applied if the random variable is discrete, and expression (45) if it is continuous. The notation M[X] is special for the mathematical expectation of the random variable that is indicated in square brackets, but the notation mх or m is sometimes used.
4. Dispersion.
Dispersion quantitatively characterizes the degree of scattering of the results of individual experiments relative to the average value. The notation for the variance of the random variable D[X] is generally accepted, but the notation ??х can also be used. Expression (46) is used to calculate the variance of a discrete random variable, and (47) is used to calculate the variance of a continuous random variable. If you take the square root of the variance, you get a value called the standard deviation (?x).
All characteristics of a random variable can be shown using Figure 22.
Figure 22 - Characteristics of a random variable
Random process is a function of time t, the value of which for any fixed value of time is a random variable. For example, Figure 23 shows a diagram of some random process observed as a result of three experiments. If we determine the value of the functions at a fixed time t1, then the resulting values will turn out to be random variables.
Figure 23 - Ensemble of implementations of a random process
Thus, the observation of any random variable (X) in time is a random process X(t). For example, information signals (telephone, telegraph, data transmission, television) and noise (narrowband and broadband) are considered as random processes.
A single observation of a random process is called implementation xk(t). The set of all possible realizations of one random process is called an ensemble of realizations. For example, Figure 23 shows an ensemble of realizations of a random process, consisting of three realizations.
To characterize random processes, the same characteristics are used as for random variables: probability distribution function, probability density function, mathematical expectation and dispersion. These characteristics are calculated in the same way as for random variables. There are random processes various types. However, in telecommunications, most random signals and noise are stationary ergodic random processes.
A stationary process is a random process whose characteristics F(x), P(x), M[X] and D[X] do not depend on time.
Ergodic is a process in which time averaging of one of the implementations leads to the same results as static averaging over all implementations. Physically, this means that all implementations of an ergodic process are similar to each other, therefore measurements and calculations of the characteristics of such a process can be carried out using one (any) of the implementations.
In addition to the four characteristics given above, random processes are also described by the correlation function and power spectral density.
The correlation function characterizes the degree of relationship between the values of a random process in various moments time t and t+?. Where? time shifting.
where tн is the observation time of the implementation xk(t).
Power Spectral Density— shows the power distribution of a random process by frequency.
where?P is the power of the random process per frequency band?f.
So the observation random phenomenon in time is a random process, its occurrence is a random event, and its value is a random variable.
For example, observing a telegraph signal at the output of a communication line for some time is a random process, the appearance of its discrete element “1” or “0” at reception is a random event, and the amplitude of this element is a random variable.
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BELARUSIAN STATE UNIVERSITY
COMPUTER SCIENCE AND RADIO ELECTRONICS
Department of Metrology and Standardization
ABSTRACT
On the topic of:
« Measuring characteristics of random signals»
MINSK, 2008
Statistical measurements are methods and means of measuring the parameters and characteristics of random signals. They are based on general principles measurements of signal parameters, but have their own specifics and features arising from the theory of random processes.
Probabilistic characteristics of random signals
A signal whose instantaneous values vary randomly over time is called random. It is described by a random time function X(t). This function can be considered as an infinite collection of functions x i (t), each of which represents one of the possible implementations of X(t). Graphically, this can be represented as follows (Figure 1):
A complete description of random signals can be made using a system of probabilistic characteristics. Any of these characteristics can be determined either by averaging over the totality of realizations x i (t), or by averaging over time of one infinitely long realization.
The dependence or independence of the results of such averaging determines the following fundamental properties of random signals - stationarity and ergodicity.
A stationary signal is a signal whose probabilistic characteristics do not depend on time.
Ergodic is a signal whose probabilistic characteristics do not depend on the implementation number.
For stationary ergodic signals, averaging any probabilistic characteristic over many realizations is equivalent to averaging over time one theoretically infinitely long realization.
For practical purposes, the most important are the following probabilistic characteristics of stationary ergodic signals with implementation duration T:
Average value (mathematical expectation). It characterizes the constant component of the signal
Average power. It characterizes the average signal level
Dispersion characterizing the average power of the variable component of the signal:
Standard deviation (RMS)
The distribution function, which is defined as the integral probability that the value xi(tj) in jth moment time will be below some values of X:
For given stationary ergodic signals F x is characterized by the relative time of stay of the implementation below level X (f i -, i-th stay interval, n - number of intervals, Figure 2)
One-dimensional probability density, called the differential distribution law:
where is the distance between adjacent levels X(t), called the differential corridor;
I - th interval of the implementation being within the limits (see Figure 1.11).
Correlation function. It characterizes the stochastic (random) connection between two instantaneous values of a random signal separated by a given time interval f
Cross correlation function. It characterizes stochastic communication by instantaneous values of random signals x(t) and y(t), separated by a time interval f
From expressions (1)-(8) it is clear that all probabilistic characteristics are non-random numbers or functions and are determined by one realization of infinite duration. In practice, the duration T, called the duration of analysis, is always limited, so in practice we can not determine the characteristics themselves, but only their estimates. These experimentally obtained estimates are called static characteristics. And since it is an estimate, it means an approximation, which is characterized by errors called statistical errors.
Measurement of average power and variance
According to formula (1), the measurement of m x is reduced to the integration of a random signal over time T. Integration can be performed using an analogue
gov or digital integrating devices used in voltmeters.
When choosing the integration time T in practice, it is necessary to minimize statistical errors. This condition is met at T(f m.c. - the maximum correlation interval, beyond which signal samples can be considered practically uncorrelated).
The P x measurement is characterized by the fact that, according to formula (2), the square of the signal is averaged, therefore the P x meter contains a device with a quadratic characteristic. The problem of measuring P x is solved using an rms voltmeter with an open input. The readings of such a voltmeter are equal. Voltmeters that measure P x are subject to increased requirements in terms of broadband, the length of the quadratic section of the detection characteristic, and the averaging time T.
To measure D x, an rms voltmeter can also be used, only in accordance with formula (3) it must have a closed input. The readings of such a voltmeter according to (4) will correspond to the values of y x.
Probability distribution analysis
Relative residence time measurement method
When measuring by this method, it is more convenient to measure not the value φ i appearing in formula (7), but the value φ i ", which characterizes the time the function x(t) remains above the level x, therefore, in experimental analysis the function is determined
To determine in accordance with formula (7), it is necessary to form a differential corridor?x, as shown in Figure 3, and measure, in addition to the values of φ i ", also φ i", which characterizes the time the implementation x(t) stays above the level x+?x , and
T i =?t 1i +?t 2i = f i - f i . (10)
Analyzers that implement this method, can be either analog or digital. The block diagram of the analog analyzer is shown in Figure 3.
With the help of the control unit, the signal level necessary for the normal operation of other functional units of the meter is ensured. Comparators K1 and K2 perform the functions of amplitude selectors and have response levels x and x+?x, respectively. These levels are set by a level controller (RU) and can be changed while simultaneously ensuring a constant width of the differential corridor?x. Thus, the signals at the output of K1 and K2 have the form of pulses U1 and U2 (Figure 3), the duration of which is respectively equal to φ i "and φ i "". Forming devices FU1 and FU2 standardize these pulses in shape and amplitude. Voltages U1 and U2 allow measure and.
When measuring, voltage U1 is averaged or integrated (switch P in position “1”), and when measured using a subtraction circuit, a difference voltage U3 is formed, which is also averaged. The type of indicator device (ID) is determined by the purpose of the analyzer. For example, in panoramic analyzers, control of the response levels of comparators K1 and K2 is carried out synchronously and automatically with the sweep of the oscilloscope used as a DUT. Such a control device allows you to register graphs of functions and.
Measuring Correlation Functions
Discrete sampling method
To measure correlation functions, the multiplication method is most often used. The operating algorithm of an analog correlometer that implements the discrete sampling method follows from formulas (8) and (9). This method involves performing the following operations:
Delay of the signal under study or one of the signals for a time f;
Multiplication of delayed and undelayed signals;
Averaging the results of multiplication.
If the correlometer is digital, then the above operations must be preceded by time sampling and level quantization. Therefore, the operating algorithm of the digital correlometer will be determined by the following relations
where and are the level-quantized values of the centered realizations X(t) and Y(t) at discrete moments of time;
Time shift interval, p = 0,1,2,…;
N - number of samples.
Correlometers come in two modifications: sequential and parallel.
In digital sequential correlometers, first the value of the correlation function is calculated using formula (1.16) at p = 0, i.e. the implementation value is multiplied by itself, then a delay φ 0, (p=1) is introduced and the value of the function is determined and further calculations are carried out at p=2,3,..., up to φ m.c. . (f m.k - the maximum correlation interval, beyond which signal samples can be considered practically uncorrelated).
A parallel digital correlometer allows you to simultaneously calculate all p-values of the correlation function, but at the same time becomes a multi-channel device. Therefore, in practice, sequential correlometers are most often implemented (Figure 5).
The operation of all correlometer nodes is synchronized by the control device (CU). The delay circuit consists of p shift registers controlled by the clock pulses of the control unit. Instead of a multiplier and averager, a microprocessor can be used. The accumulation of multiplication results is carried out throughout the entire measurement cycle, and at the end of the cycle we have full information about the correlation function. This information is reproduced on the IU in the form of a correlogram. This circuit operates in the range of up to hundreds of kilohertz.
Probability distribution analysis using discrete sampling method
If a differential corridor is formed using quantization levels, and the clock pulses of the control unit are used as polling pulses, then the device, the block diagram of which is shown in Figure 5, will work as a probability distribution meter that implements the discrete sampling method.
The essence of this method is the same as the method of measuring relative residence time discussed above. However, now this comparison occurs at discrete points, which are set by sampling strobe pulses with a repetition period T 0 . These impulses are given by the control unit. The T 0 value determines the sampling step when converting the analog value x(t) into a discrete one.
The number of pulses corresponding to the number of samples n is accumulated in the averager during time T. Having denoted, we obtain the following expressions after substituting into formulas (1.14) and (1.11):
After processing the value is reproduced on the indicator device.
The basic error of the device in all modes does not exceed ±5%.
LITERATURE
1 Metrology and electrical measurements in telecommunication systems: Textbook for universities / A.S. Sigov, Yu.D. Belik. and others / Ed. IN AND. Nefedova. - 2nd ed., revised. and additional - M.: Higher. school, 2005.
2 Baklanov I.G. Measurement technologies in modern telecommunications. - M.: ECO-TRENDS, 2007.
3 Metrology, standardization and measurements in communication technology: Textbook. manual for universities / Ed. B.P. Lame. - M.: Radio and communication, 2006.
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A random signal is described by a random time function X(t). This function can be considered as an infinite collection of functions x i (t), each of which represents one of the possible implementations of X(t). Graphically, this can be represented as follows (Figure 1):
Picture 1
A complete description of random signals can be made using a system of probabilistic characteristics. Any of these characteristics can be determined either by averaging over the totality of realizations x i (t), or by averaging over time of one infinitely long realization.
The dependence or independence of the results of such averaging determines the following fundamental properties of random signals - stationarity and ergodicity.
A stationary signal is a signal whose probabilistic characteristics do not depend on time.
Ergodic is a signal whose probabilistic characteristics do not depend on the implementation number.
For stationary ergodic signals, averaging any probabilistic characteristic over many realizations is equivalent to averaging over time one theoretically infinitely long realization.
For practical purposes, the most important are the following probabilistic characteristics of stationary ergodic signals with implementation duration T:
Average value (mathematical expectation). It characterizes the constant component of the signal
Average power. It characterizes the average signal level
Dispersion characterizing the average power of the variable component of the signal:
Standard deviation (RMS)
Distribution function, which is defined as the integral probability that the value xi(tj) at the j-th moment of time will be lower than some values of X:
For given stationary ergodic signals F x is characterized by the relative time of stay of the implementation below level X (f i -, i-th stay interval, n - number of intervals, Figure 2)
Figure 2
One-dimensional probability density, called the differential distribution law:
where is the distance between adjacent levels X(t), called the differential corridor;
I - interval of stay of the implementation within the limits (see Figure 2).
Correlation function. It characterizes the stochastic (random) connection between two instantaneous values of a random signal separated by a given time interval f
Cross correlation function. It characterizes stochastic communication by instantaneous values of random signals x(t) and y(t), separated by a time interval f
From expressions (1)-(8) it is clear that all probabilistic characteristics are non-random numbers or functions and are determined by one realization of infinite duration. In practice, the duration T, called the duration of analysis, is always limited, so in practice we can not determine the characteristics themselves, but only their estimates. These experimentally obtained estimates are called static characteristics. And since it is an estimate, it means an approximation, which is characterized by errors called statistical errors.
probabilistic ergodic random discrete
1. Features of the study of self-propelled guns under random impacts
With deterministic predetermined influences, the state of the ACS at any moment t is determined by the initial state of the system at some point in time t0 and the influences applied to the system. This problem is determined by solving the corresponding differential equation
anx (n)+an-1x(n-1)+…+a0x=bmg(m)+bm-1g(m-1)+…+b0g. (26.1)
If ai, bj are constant coefficients, and g is a certain function of time, then the solution of this equation for given initial conditions will be unique and defined for the entire time interval.
However, in real conditions often external influences change randomly, i.e. in a way that was not foreseen in advance. For example:
daily changes in power system load;
gusts of wind acting on the aircraft;
wave impacts in hydrodynamic systems;
radar signals;
noise in radio devices, etc.
Random influences can be applied to the system from the outside (external influences) or arise within some of its elements (internal noise).
Obviously, if in equation (26.1) g - the input action is not predetermined, i.e. is a random function, or the system parameters ai, bj change randomly, then it is impossible to obtain a solution to this equation in a deterministic (i.e., defined) form.
Of course, you can set some maximum values of these parameters and solve the problem posed (calculating the system for a given accuracy with maximum values of random influences). But since the maximum values of a random variable are rarely observed, in this case, obviously more stringent requirements will be imposed on the system than is caused by reality.
True, such an approach is sometimes the only possible one (high-precision production, otherwise - defective). Therefore, in most cases, the calculation of a system under random influences is carried out not according to the maximum, but according to the most probable value of random variables, i.e. according to the value that occurs most often.
In this case, the most rational technical solution is obtained (smaller system gain, smaller dimensions of individual devices, lower energy consumption), although for unlikely values of the reference action there will be a deterioration in the operation of the system.
Calculation of ACS under random influences using special statistical methods that operate with the statistical characteristics of random influences, which are not random, but deterministic quantities.
An ACS designed on the basis of statistical methods will provide the appropriate requirements not for one, deterministic impact, but for a whole set of these impacts specified using statistical characteristics (if the ACS error is random in nature, then its exact value at any point in time at cannot be obtained by statistical calculation).
Statistical methods for calculating automatic control systems are based on calculations and works of Soviet scientists: Khinchin A.Ya., Kolmogorov A.N., Gnedenko V.V., Solodovnikova V.V., Pugacheva V.S., Kazakova I.E. and others, as well as foreign scientists - N. Wiener, L. Zade, J. Ragotsine, Kalman, Bucy and others.
2. Brief information about random processes.
A random function is a function that, for each value of the independent variable, is a random variable. Random functions for which the independent variable is time t are called random processes. Since processes in automatic control systems occur over time, in the future we will consider only random processes.
The random process x(t) is not a specific curve, it is a set of specific curves x i (t) (i=1,2,...,n), obtained as a result of individual experiments (Fig. 26.1). Each curve of this set is called a realization of a random process, and it is impossible to say which of the realizations the process will follow.
Rice. 26.1. Graphs of realizations and mathematical expectation of a random process
For a random process, as well as for a random variable, to determine statistical properties, the concept of distribution function (integral distribution law) F(x, t) and probability density (differential distribution law) w(x, t) is introduced. These characteristics depend on a fixed observation time t and on a certain selected level x, that is, they are functions of two variables - x and t.
The functions F(x, t) and w(x, t) are the simplest statistical characteristics of a random process. They characterize the random process in isolation in individual sections, without revealing the connection between the sections of the random process.
The main characteristics of random processes most widely used in the study of control systems include: mathematical expectation, dispersion, mean value of the square of a random process, correlation function, spectral density and others.
A. Expected value m x (t) is the average value of the random process x(t) over the set and is defined
(26.2)
where w 1 (x, t) - one-dimensional probability density of a random process x(t) .
The mathematical expectation of a random process x(t) is a certain non-random function of time m x (t), around which all implementations of a given random process are grouped and around which they fluctuate (Fig. 26.1).
The average value of the square of a random process is the quantity
(26.3)
A centered random process is often introduced into consideration, which is understood as the deviation of the random process X(t) from its average value m x (t), or
(26.4)
B. Dispersion. To take into account the degree of scattering of realizations of a random process relative to its average value, the concept of dispersion of a random process is introduced, which is equal to the mathematical expectation of the square of a centered random process
(26.5)
The variance of a random process is a non-random function of time D x (t) and characterizes the spread of the random process X (t) relative to its mathematical expectation m x (t).
In practice, statistical characteristics that have the same dimension as a random variable are widely used, which include:
Mean square value of a random process
equal to the value of the square root of the average value of the square of the random process;
Standard deviation of a random process
(26.7)
equal to the square root of the variance of the random process.
Expectation and dispersion are important characteristics of a random process, but do not provide sufficient insight into the internal connections of a random process, which have a significant impact on the nature of its implementation within a given time interval.
One of the statistical characteristics that reflects the characteristics of the internal connections of a random process is the correlation function.
IN. Correlation function random process X(t) is a non-random function of two arguments R x (t 1 ,t 2), which for each pair of arbitrarily chosen values of time points t 1 and t 2 is equal to the mathematical expectation of the product of two random variables -X(t 1) and X (t 2), corresponding sections of the random process:
where w 1 (x 1, t 1, x 2, t 2) is a two-dimensional probability density.
Random processes, depending on how their statistical characteristics change over time, are divided into stationary and non-stationary. There are stationarity in the narrow and broad sense.
A random process X(t) is called stationary in the narrow sense if its n-dimensional distribution functions and probability density for any n do not depend on the position of the time reference t.
Stationary in the broad sense is a random process X(t), the mathematical expectation of which is constant:
М[Х(t)]= m x =const, (26.9)
and the correlation function depends on only one variable - the difference in arguments t=t 2 -t 1:
In the theory of random processes, two concepts of average values are used: the average value over a set and the average value over time.
The average value over the set is determined based on observation of many implementations of a random process at the same point in time, i.e.
(26.11)
The time average is determined based on observations of a separate implementation of random x(t) over a sufficiently long time T, i.e.
(26.12)
It follows from the ergodic theorem that for the so-called ergodic stationary random processes, the average value over the set coincides with the average value over time, i.e.
(26.13)
In accordance with the ergodic theorem for a stationary random process with mathematical expectation m 0 x =0, the correlation function can be defined
where x(t) is any realization of a random process.
The statistical properties of the connection between two random processes X(t) and G(t) can be characterized by the cross-correlation function R xg (t 1 ,t 2), which for each pair of arbitrarily chosen values of the arguments t 1 and t 2 is equal to
According to the ergodic theorem, instead of (26.15) we can write
(26.16)
where x(t) and g(t) are any implementations of stationary random processes X(t) and G(t).
If the random processes X(t) and G(t) are not statistically related to each other and have average values equal to zero, then their mutual correlation function for all t is equal to zero.
Let us present some properties of correlation functions.
1. The initial value of the correlation function is equal to the average
the value of the square of the random process:
2. The value of the correlation function at any t cannot exceed its initial value, that is
3. The correlation function is an even function of t, i.e.
(26.18)
Another statistical characteristic reflecting internal structure stationary random process X(t), is the spectral density S x (w), which characterizes the energy distribution of a random signal over the frequency spectrum.
G. Spectral Density S x (w) of the random process X(t) is defined as the Fourier transform of the correlation function R(t),
(26.19)
Hence,
since the spectral density S x ( a) is a real and even function of frequency w.
Relations (26.19) and (26.20) allow us to establish some dependencies between the structure of the random process X(t) and the type of characteristics R x (t) and S x (w) (Fig. 26.2).
It follows from the above graphs that with an increase in the rate of change in the realization of X(t), the correlation function R x (t) narrows (sharpenes), and the spectral density S x (w) expands.
The invention relates to computer technology and control systems and can be used to construct adaptive fuzzy controllers for solving problems of controlling objects, the mathematical model of which is not a priori defined, and the purpose of operation is expressed in fuzzy concepts. The purpose of the invention is to expand functionality. The probabilistic automaton contains: a first memory block 2, a second memory block 3, a state selection block 6, a third memory block 7, a first switch 9, an output signal selection block 10, a second switch 12, a clock pulse generator 13, a first random code generation block 14, the second block for generating a random code 15, the fourth block of memory 16, the first block for determining the maximum code 18, the fifth block of memory 20, the second block for determining the maximum code 22. 6 s.p. f-ly, 21 ill.
The invention relates to computer technology and control systems and can be used to construct adaptive fuzzy controllers for solving problems of controlling objects, the mathematical model of which is not a priori defined, and the purpose of operation is expressed in fuzzy concepts. A probabilistic automaton is known (AS USSR N 1045232, class G 06 F 15/36, 1983), containing a random code generation unit, a state selection unit, a clock pulse generator, an AND element, a switch, a memory unit, and a waiting time setting unit , OR element, random voltage generator, wherein the group of outputs of the random code generation block is connected to the inputs of the group of information inputs of the state selection block, the group of outputs of which is connected to the group of information inputs of the switch, the group of outputs of which is connected to the group of inputs of the memory block, the group of outputs of which is connected to a group of control inputs of the state selection block and with a group of inputs of the block for setting the waiting time, the group of outputs of which is connected to the group of outputs of the machine and to the inputs of the OR element, the output of which is connected to the inverse input of the AND element and to the first clock input of the random code generation block, the output of the clock generator pulses are connected to the first clock input of the block for setting the waiting time and to the direct input of the AND element, the output of which is connected to the clock input of the switch, to the second clock input of the random code generation block and to the second clock input of the block for setting the waiting time, the output of the random voltage generator is connected to the input control unit for setting the waiting time. Features that coincide with the features of the proposed technical solution are a random code generation unit, a state selection unit, a clock pulse generator, a switch, and a memory unit. The disadvantage of this device is its limited functionality, since this device does not have the ability to compare the state of the machine with the qualitative characteristics of the latter. The reasons preventing the achievement of the required technical solution lie in the particular implementation of the known device, in which it is possible to generate states and output signals only in clear terms. A probabilistic automaton is known (AS USSR N 1108455, class G 06 F 15/20, 1984), containing a first memory block, a state selection block, a random code generation block, a clock pulse generator, a switch, a second memory block, and the inputs groups of control and setting inputs of the first memory block are connected, respectively, to the outputs of groups of control inputs and groups of setting inputs, and a group of inputs is connected to the first group of information inputs of the state selection block, the group of outputs of which is connected to the first group of information inputs of the state selection block, the second group of information inputs which is connected to the group of outputs of the random code generation block, the group of outputs of which is connected to the group of inputs of the switch, the group of outputs of which is connected to the group of inputs of the second memory block, the group of outputs of which is connected to the outputs of the device and to the group of control inputs of the state selection block, the output of the clock pulse generator connected to the clock inputs of the random code generation unit and the switch. Features that coincide with the features of the proposed technical solution are a random code generation unit, a state selection unit, a first memory unit, a clock generator, a switch, and a second memory unit. The disadvantage of this device is its limited functionality due to the fact that with a fuzzy definition of output states, the device does not allow defining fuzzy sets of qualitative characteristics of these signals on a clear set (output signals). The reasons preventing the achievement of the required technical solution are particularly the implementation of a probabilistic automaton, in which states and output signals belonging to clearly defined sets are generated. Of the known devices, the closest to the claimed fuzzy probabilistic automaton in terms of a set of design and functional features is a probabilistic automaton (AS USSR N 1200297, class G 06 F 15/20, 1985), containing a first memory block, a state selection block, a block random code generation, a switch, a second memory block, an output signal selection block, a third memory block, a clock pulse generator, wherein the inputs of the groups of control and installation inputs of the first memory block are connected, respectively, to the inputs of the groups of control inputs and groups of installation inputs, and the group of outputs is connected to the first group of information inputs of the state selection block, the group of outputs of which is connected to the first group of inputs of the switch, the group of outputs of which is connected to the group of installation inputs of the second memory block, the group of outputs of which is connected to the group of control inputs of the state selection block and to the first group of control inputs of the output selection block signal, the group of outputs of which is connected to the group of inputs of the third memory block, the group of outputs of which is connected to the group of outputs of the device, the output of the clock pulse generator is connected to the clock inputs of the switch, the output signal selection block and the random code generation block, the group of outputs of which is connected to the second group of information inputs of the state selection block. Features that coincide with the features of the proposed technical solution are a random code generation unit, a state selection unit, a first memory unit, a clock generator, a switch, a second memory unit, an output signal selection unit, and a third memory unit. The disadvantage of the known device is its limited functionality caused by the fact that the known device cannot be used to solve problems of modeling and controlling objects that have a priori uncertainty and a fuzzy (qualitative) description of the parameters and purpose of the simulation. This is primarily due to the fact that the known device does not perform the function of establishing a correspondence between clear concepts (sets of outputs and inputs) and fuzzy concepts (qualitative characteristics of inputs and outputs), specified in the form of fuzzy variables. The reasons preventing the achievement of the required technical solution are the particular implementation of the probabilistic automaton, in which states and output signals belonging to clearly defined sets are generated in accordance with the given functions of transitions and outputs for modeling problems of stochastic objects. The problem to be solved by the invention is the ability to generate states and output signals in accordance with specified functions of transitions and outputs, as well as generate fuzzy variables specified on sets of states and output signals in accordance with expert estimates for further use in modeling problems and control of complex objects in the absence of a priori information about the mathematical model. To achieve the technical result, which consists in expanding the functionality by generating fuzzy variables specified on sets of states and output signals using expert information, a fuzzy probabilistic automaton is proposed, containing a clock pulse generator, a first random code generation block, a state selection block, an output signal selection block, the first, second and third blocks and a switch, wherein the M outputs of the group of control inputs of the device are connected to the M inputs of the first groups of control inputs of the first memory block, the inputs (NxNxM) of the groups of the first installation inputs of the device are connected, respectively, to the inputs (NxNxM) of the groups installation inputs of the first memory block, N inputs of the second control input groups of which are connected to N outputs of the group of outputs of the third memory block, the output of the first clock generator is connected to the clock inputs of the first random code generation block, K outputs of the group of outputs of which are connected to K inputs of the second information group inputs of the state selection block, additionally introduce the second random code generation block, the fourth and fifth memory blocks, the second switch, the first and second blocks for determining the maximum code, and the inputs (NxPxM) of the installation input groups of the second memory block are connected to the inputs (NxPxM) of the second installation groups inputs of the device, M inputs of the group of first control inputs are connected to M inputs of the group of control inputs of the device and to M inputs of the group of first control inputs of the first memory block, N inputs of the group of second control inputs are connected to N inputs of the group of second control inputs of the first memory block, N outputs of the group outputs of the third memory block and N inputs of the group of control inputs of the first switch, outputs of P groups of information outputs are connected to the corresponding inputs of P groups of information inputs of the output signal selection block, and the clock input is connected to the output of the clock pulse generator and to the clock inputs of the first memory block, the first and of the second random code generation block, N outputs of the group of information outputs of the state selection block are connected to the corresponding N inputs of the group of first information inputs of the third memory block, K outputs of the group of outputs of the second random code generation block are connected to K inputs of the group of second information inputs of the output signal selection block, output (NxL) groups of information inputs of the first switch are connected to the outputs (NxL) of groups of information outputs of the fourth memory block, (NxL) groups of information inputs of which are connected to the inputs (NxL) of the third groups of installation inputs of the device, outputs of L groups of information outputs of the first switch are connected to the inputs L groups of information inputs of the first block for determining the maximum code, the outputs of the group of information outputs of which are connected to the outputs of the third group of outputs of the device, P outputs of the group of outputs of the output signal selection block are connected to P inputs of the group of control inputs of the second switch, the inputs (PxF) of the groups of information inputs of which are connected with the outputs (PxF) of the groups of information outputs of the fifth memory block, the inputs (PxF) of the groups of information inputs of which are connected to the inputs (PxF) of the fourth groups of installation inputs of the device, the outputs P of the groups of information outputs of the second switch are connected to the inputs F of the groups of information inputs of the second block for determining the maximum code, the groups of information outputs of which are connected to the outputs of the fourth group of device outputs. The presence of a cause-and-effect relationship between the technical results and the features of the claimed invention is proven by the following logical premises. And the basis for the operation of a probabilistic automaton is the assumption that the formal specification of a fuzzy probabilistic automaton (FPA) can be represented in the form where X, Y, Z are, respectively, a set of input and output signals 
- a set of conditional probabilities that determine whether the NVA is in the state z t at a time step t, provided that the signal x t is supplied to the input in this clock cycle and the NVA is in the state in the previous (t-1) step 
- a set of conditional probabilities that determine the presence of a signal y t at the output of the machine, provided that there is a signal x t at the output in this clock cycle and the NVA is in the state x t-1 in the previous (t-1) clock cycle; linguistic variable (LP) “selection of state”, specified by the set (,T(),Z), where is the name of the LP, T () is the term set of the LP, Z is the base set; LP “selection of output parameter”, specified by the set (,T(),Y), where is the name of the LP, T () is the term set of the LP, Y is the base set. If and are linguistic variables, and T() = ( 1 ,..., L ) and T() = ( 1 ,..., F ) is a term set, where 
- names of the NP, then using an expert survey you can set and - membership functions of fuzzy variables. A fuzzy probabilistic automaton generates states, output signals, as well as linguistic variables defined on sets of states and output signals. In fig. 1 and fig. 2 shows a diagram of the proposed object; in fig. 3 - functional diagram of the first memory block 2; in fig. 4 - functional diagram of the second memory block 3; in fig. 5 - block diagram of state selection block 6; in fig. 6 - functional diagram of the third memory block 7; in fig. 7 - functional diagram of the first switch 9; in fig. 8 - functional diagram of the block for selecting the output signal 10; in fig. 9 - functional diagram of the second switch 12; in fig. 10 is a functional diagram of the first random code generation block 14; in fig. 11 is a functional diagram of the second random code generation block 15; in fig. 12 is a block diagram of the fourth memory block 16; in fig. 13 is a functional diagram of the first block for determining the maximum code 18; in fig. 14 is a block diagram of the fifth memory block 20; in fig. 15 is a functional diagram of the second block for determining the maximum code 22; in fig. 16 is a functional diagram of the decoder of the first block for determining the maximum code; in fig. 17 is a functional diagram of each of the comparison blocks of the first maximum code determination block; FIG. 18 - functional diagram of the decoder of the second block for determining the maximum code; in fig. 19 is a functional diagram of each of the comparison blocks of the second maximum code determination block; in fig. 20 - graphs of membership functions of fuzzy variables 1, 2,..., L; in fig. 21 - graphs of membership functions of fuzzy variables 1, 2,..., F. The block diagram of a fuzzy probabilistic automaton (Fig. 1 and 2) contains: 1 1 -1 M - group of control inputs; 2 - first memory block; 3 - second memory block; 
- (NxNxM) groups of first installation inputs; (NxPxM) - groups of second installation inputs; 6 - state selection block; 7 - third memory block; 8 1 -8 N - group of outputs of the third memory block 7 and control inputs of the first switch 9; 10 - output signal selection block; 11 1 -11 P - group of second outputs of the device and control inputs of the second switch 12; 13 - clock generator; 14 - first random code generation block; 15 - second random code generation block; 16 - fourth memory block; , (NxL) groups of third groups of device installation inputs; 18 - first block for determining the maximum code; 19 1 - 19 L - outputs of the third group of device outputs; 20 - fifth memory block; - (PxF) groups of fourth installation inputs of the device; 22 - second block for determining the maximum code; 23 1 -23 F - outputs of the fourth group of device outputs. The functional diagram of the first memory block 2 (Fig. 3) contains: - M inputs of the first group of control inputs; 
- (MxNxN) groups of installation inputs; - N inputs of the second group of control inputs; 
- registers; (25 1m i1 -25 Km iN) - (NxM) groups of elements AND; 26 - clock input; - (MxN) groups of outputs of elements AND 25 and (MxN) groups of inputs (MxN) groups of elements OR 
outputs N groups of outputs of memory block 2. The functional diagram of the second memory block 3 (Fig. 4) contains: - M - groups of inputs of the first group of control inputs; - N inputs of the second group of control inputs; - (MxNxP) groups of first installation inputs; 26 - clock input; 
- registers; (31 1m ip -31 Km ip) - (NxP) groups of elements AND; (32 1m ip -32 Km ip) - (MxN) groups of outputs of elements AND 32 and groups of inputs of elements OR - outputs P groups of outputs of memory block 3. The block diagram of the state selection block 6 (Fig. 5) contains: 
- N group of inputs of the first group of information inputs; - N comparison nodes; 36 1 - 36 K - inputs of the second group of information inputs; - N outputs of the state selection block 6; 38 1 - 38 N-1 - elements AND. The block diagram of the third memory block 7 (Fig. 6) contains: 8 1 - 8 N - outputs; 37 1 - 37 N - group of inputs; 38 1 - 38 N - triggers; 39 1 - 39 N - OR elements. The functional diagram of the first switch 9 (Fig. 7) contains: - N groups of control inputs; - (LxN) groups of elements AND, D elements in each; 
- (LxN) groups of D-bit information inputs; 
- L group of OR elements, D elements each; 
- L groups D - bit outputs of the first switch 9. The functional diagram of the output signal selection block 10 (Fig. 8) contains: - outputs; 
inputs of the first group of information inputs; - comparison nodes; 45 1 - 45 K - inputs of the second group of information inputs; 46 1 - 46 p-1 - elements P. The functional diagram of the second switch 12 (Fig. 9) contains: - P groups of inputs, groups of control inputs; 
(FxP) groups of elements AND, with D elements in each; 
(FxP) groups D - bit inputs of a group of information inputs; 
- F groups of OR elements, D elements in each; 50 1 f -50 D f - F groups D - bit outputs of the second switch 12. The functional diagram of the first random code generation block 14 (Fig. 10) contains: 36 1 - 36 K - outputs; 51 - clock input; 52 - first element AND; 53 1 - 53 Z second elements AND; 54 - code converter; 55 - generator of Poisson pulse flow; 56 - cyclically closed shift register. The functional diagram of the second random code generation block 15 (Fig. 11) contains: 45 1 - 45 K - outputs; 51 - clock input; 57 - first element AND; 58 1 - 58 Z - second elements And; 59 - code converter; 60 - generator of Poisson pulse flow; 61 - cyclically closed shift register. The block diagram of the fourth memory block 16 (Fig. 12) contains: 
- (LxN) groups D - bit information inputs; 62 1i - (LxN) register groups; 41 1 l i -41 D l i - (LxN) groups D - bit outputs of block 16. The functional diagram of the first block for determining the maximum code 18 (Fig. 13) contains: 19 1 - 29 L - group of outputs; 
- L groups D - bit inputs; - a group of registers; 65 1 - 64 D group of state decoders; 65 1 l -65 D l - L groups of elements AND, D elements in each; 66 1 - 66 D - group of analysis nodes; 67 1 - 67 L - group of OR elements. The block diagram of the fifth memory block 20 (Fig. 14) contains: 
(FxP) groups D - bit information inputs; 68 fp - 68 fp - F groups of registers, P in each group; 
- (FxP) groups D - bit outputs. The functional diagram of the second block for determining the maximum code 22 (Fig. 15) contains: 23 1 - 23 F - group of outputs; 
- F groups D - bit inputs; 69 1 - 69 F - register group; 70 1 - 70 D - group of state decoders; 
- F groups of elements AND, D elements in each; 72 1 - 72 D - analysis nodes; 73 1 - 73 F - group of OR elements. The functional diagram of the decoder of the first block for determining the maximum code (Fig. 16) contains 
- first groups of inputs; 
- groups of OR elements, L - elements in each; 76 1 - 76 D - first elements And; 
- second groups of inputs; 78 1 - 78 D - second elements And; 
- groups of decoder outputs 64. The functional diagram of each of the d, analysis nodes 66 of the first block for determining the maximum code 18 (Fig. 17) contains 
- D-1 groups of the first L - bit inputs; 
- D-1 groups of second L - bit inputs; 
- D-1 first groups of And elements, L elements And in each; 
- D-1 first groups of OR elements, L OR elements in each; 
- D-1 groups of second OR elements, L OR elements in each 
- D-1 second groups of elements AND, L elements in each; 
- D-1 second groups of NOT elements, L elements in each group; 
- D-1 third groups of elements AND, L elements in each; 
- D-1 third groups of OR elements, L elements in each group; 
- D-1 fourth groups of elements AND, L elements in each; 
- D-1 groups L - bit outputs; 
- D-1 groups of third L - bit inputs; 
- D-1 second groups of NOT elements, L in each group; 
- D-1 third groups of elements NOT, L in each group. The functional diagram of the decoders 70 of the second block for determining the maximum code 22 (Fig. 18) contains: 
- first groups of inputs; 
- groups of OR elements, F elements each; 94 1 - 94 D - first elements And; 
- second groups of inputs; 96 1 - 96 D - second elements And; 
- D groups of decoder outputs. The functional diagram of each of the d analysis nodes 72 of the second block for determining the maximum code 22 (Fig. 19) contains: 
- D-1 groups of the first F - bit inputs; 
- D-1 groups of second F - bit inputs; 
- D-1 first groups of And elements, F elements And in each; 
- D-1 first groups of OR elements, F OR elements in each; 
- D-1 groups of second OR elements, F OR elements in each; 
- D-1 second groups of elements AND, F elements in each; 
- D-1 second groups of NOT elements, F elements in each group; 
- D-1 third groups of elements And, F elements in each; 
- D-1 third groups of OR elements, F elements in each group; 
- D-1 fourth groups of elements AND, F elements in each; 
- D-1 groups F - bit outputs; 
- D-1 groups of third F - bit inputs; 
- D-1 second groups of NOT elements, F in each group; 
- D-1 third groups of elements NOT, F in each group. The elements of a fuzzy automaton are interconnected as follows. The inputs of the group of control inputs 1 1 - 1 M of the device are connected to the inputs of the first groups of control inputs of the first memory block 2 and the second memory block 3, inputs 
(NxNxM) - groups of first installation inputs of the device are connected respectively to the inputs of groups of installation inputs of the first memory block 2, inputs (NxPxM) - groups of second installation inputs of the device are connected to the inputs of groups of installation inputs of the second memory block 3, outputs N groups of information outputs of the first memory block 2 are connected to the corresponding inputs of N groups of the first group of information inputs of the state selection block 6, the outputs of the group of information outputs of the state selection block 6 are connected to the corresponding inputs of the group of information inputs of the third memory block 7, outputs 8 1 - 8 N of the group of outputs of the third memory block 7 are connected to corresponding inputs 8 1 - 8 N of the group of control inputs of the first switch 9, with inputs of groups of second control inputs of the first 2 and second 3 memory blocks, and with outputs 8 1 - 8 N of the first group of device outputs, outputs P of groups of information outputs of the second memory block 3 connected to the corresponding inputs P of the groups of information inputs of the output signal selection block 10, outputs 11 1 - 11 P of the group of control outputs of which are connected to the corresponding inputs 11 1 - 11 P of the group of control inputs of the second switch 12, with outputs 11 1 - 11 P of the second group of outputs device, the output of the clock pulse generator 13 is connected to the clock inputs of the first 2 and second 3 memory blocks, the first 14 and second 15 random code generation blocks, the outputs of group K of information outputs of the first random code generation block 14 are connected to the corresponding inputs of the second group of information inputs of the selection block states 6, the outputs of the group of outputs of the second random code generation block 15 are connected to the corresponding inputs of the second group of information inputs of the output signal selection block 10, the inputs (NxL) of the groups of second information inputs of the first switch 9 are connected to the outputs (NxL) of the groups of information outputs of the fourth memory block 16 , (NxL) groups of information inputs of which are connected to the inputs (NxL) of the third groups of installation inputs 
devices, the outputs of L groups of information outputs of the first switch 9 are connected to the inputs of L groups of information inputs of the first block for determining the maximum code 18, the outputs of the group of information outputs of which are connected to outputs 19 1 - 19 L of the third group of outputs of the device, inputs (PxF) of groups of second information inputs of the second switch 12 are connected to the outputs (PxF) of the information output groups of the fifth memory block 20, the inputs (PxF) of the information input groups of which are connected to the inputs (PxF) of the fourth groups of installation inputs 
device, the outputs F of groups of information outputs of the second switch 12 are connected to the inputs F of groups of information inputs of the second block for determining the maximum code 22, the groups of information outputs of which are connected to outputs 23 1 - 23 F by the fourth group of outputs of the device. The first memory block 2 has each of the K inputs 
The (i, j, m)-th group of installation inputs are connected to the write inputs of the corresponding registers 24 1m ij , register outputs 
connected to the first inputs of the corresponding AND elements (25 1m i1 -25 Km i1)-(25 1m iN -25 Km iN) (im)-th group, the second inputs of the AND elements are combined and connected to the clock input 26 of memory block 2, the third inputs AND elements 25 1m 11 -25 Km NN of each of the m groups are combined and connected to the m inputs 1 m of the group of the first control inputs of the first memory block 2, the fourth inputs of the AND elements (25 1m i1 -25 Km i1)-(25 1m iN -25 Km iN) (i m-th groups s are combined and connected to the i-th input 8 i of the second group of control inputs of memory block 2, the outputs of the AND element 25 are connected to the corresponding inputs (N x M) of the groups of OR elements 
, the outputs of which are connected, respectively, to the outputs of N groups of outputs 29 1 j -29 K j of memory block 2. In the second memory block 3, each of the K inputs of the (i, p, m) group of installation inputs is connected to the write inputs of the corresponding registers 30 m i p , the outputs of registers 30 m i 1 -30 m i P are connected to the first inputs of the corresponding AND elements (31 1m i1 -31 Km i1)-(31 1m iP -31 Km iP) (im)-th group, the second inputs of the AND elements are combined and connected with clock input 26 of memory block 2, third inputs of AND elements 31 1m i1 -31 Km NP of each of m groups are combined and connected to m inputs 1 m of the first group of control inputs of the second memory block 3, fourth inputs of AND elements (31 1m i1 -31 Km i1)-(31 1m iP -31 Km iP) (im)-th groups are combined and connected to the i-th input 8 i of the second group of control inputs of memory block 3, the outputs of elements AND 31 are connected to the corresponding inputs (N x M) groups of elements OR 
, the outputs of which are connected respectively to the outputs P of output groups 34 1 p -34 K p of memory block 3. In the state selection block there are 6 inputs 
the first groups of information inputs are connected to the inputs of the first groups inputs j-x comparison nodes 35 j, the same inputs of the second groups of inputs of which are combined and connected to the corresponding inputs 36 1 -36 K of the second group of information inputs of the state selection block 6, the output of the comparison node 35 1 is connected to the output 37 1 of block 6 and to the first inverse inputs of the AND elements 38 1 -38 N-1, the outputs of the comparison nodes 35 i are connected to the direct inputs of the corresponding elements AND 38 i-1 and with the i-and inverse inputs of the elements AND 38 i 37 i+1 of block 6. In the third memory block 7 inputs 37 1 - 37 N are connected to the single inputs of the corresponding flip-flops 38 1 - 38 N, the zero inputs of which are connected to the outputs of the corresponding elements OR 39 1 - 39 N, and the single outputs are connected to the outputs 8 1 - 8 N of block 7 and the corresponding inputs of the corresponding elements OR 39 1 - 39 N, and the single output of the trigger 38 i is connected to the output 8 i of block 7 and to the corresponding inputs of the OR elements 39 1 - 39 i-1, 39 i+1 - 39 N. In the first switch 9 i-th inputs 8 i of the group of control inputs are connected to the first inputs of AND elements 
groups of information inputs, outputs of elements AND 
, the outputs of which are connected to the outputs 
first switch 9. In the output signal selection block there are 10 inputs 
the first group of information inputs are connected to the inputs of the first groups of inputs p-th nodes comparison 44 P, the same inputs of the second groups of inputs of which are combined and connected to the corresponding inputs 45 1 - 45 K of the second group of information inputs of the output signal selection block 10, the output of the comparison node 44 1 is connected to the output 1 1 of the block and to the first inverse inputs of elements AND 46 1 - 46 p-1, the outputs of the comparison nodes 44p are connected to the direct inputs of the corresponding elements AND 46 p-1 and to the p- and inverse inputs of the elements AND 46 p, the outputs of which are connected to the outputs 11 p+1 of block 10. In the second switch 12 p-th inputs 11 p groups of control inputs are connected to the first inputs of elements B 
groups whose second inputs are connected to the inputs 
groups of information inputs, outputs of AND elements 
connected to the corresponding inputs of the OR elements 
, the outputs of which are connected to the outputs 
the second switch 12. In the first random code generation block 14, the clock input 52 is connected to the inverse input of the first And element 52 and to the first inputs of the second And elements 53 1 - 53 Z, the outputs of which are connected to the corresponding inputs of the code converter 54, the outputs of which are connected to the outputs 36 1 - 36 K block, the output of the Poisson pulse stream generator 55 is connected to the direct input of the first element AND 52, the output of which is connected to the clock input of a cyclically closed shift register 56, the bit outputs of which are connected to the second inputs of the corresponding elements AND 53 1 - 53 Z. In the second random code generation block 15, clock input 51 is connected to the inverse input of the first And element 57 and to the first inputs of the second And elements 58 1 - 58 Z, the outputs of which are connected to the corresponding inputs of the code converter 59, the outputs of which are connected to outputs 45 1 - 45 K block, the output of the Poisson pulse stream generator 60 is connected to the direct input of the first element AND 57, the input of which is connected to the clock input of a cyclically closed shift register 61, the bit outputs of which are connected to the second inputs of the corresponding elements AND 58 1 - 58 Z. In the fourth memory block there are 16 inputs 17 1 1 i -17 D l i (l, i)-groups of installation inputs 
connected to the corresponding inputs of the (li) registers 62 li 
, the outputs of which are connected respectively to the outputs of the (l, i)-th group of outputs of block 16. In the first block 18 for determining the maximum code, the inputs of l groups 
connected to the write inputs of registers 63 l, straight d-e the outputs of which are connected to the first group of inputs of the 64 d decoders and to the first inputs of the AND element 
, the first inverse outputs of registers 63 l are connected to the first inputs of the second group of inputs of the decoder 64 1, the remaining inverse outputs of registers 63 l are connected to the inputs of the second group of inputs of decoder 64 d and to the first groups of inputs of the (D-1) analysis nodes 66 d, the output groups of the first decoder 64 1 are connected to the second group of inputs of the analysis node 66 1 b the output groups of the remaining decoders 64 d are connected to the third groups of inputs of the analysis nodes 66 d, outputs d-nodes analysis 66 d are connected to the second group of inputs (d+1)-th analysis nodes 66 j+1, L outputs (D-1)-th analysis node 66 D-1 
the outputs of elements AND 65 1 l -65 K l are connected to the inputs of elements OR 67 l, the outputs of which are connected to the outputs 19 l of the block for issuing the maximum code 18. In the fifth memory block there are 20 inputs 
(f, p) groups of information inputs are connected to the corresponding inputs (fp) - 68 fp registers 
groups whose outputs are connected respectively to the outputs 
(f, p)-th group of outputs of block 20. In the second block 22 of determining the maximum code, the inputs of f groups of information inputs 
connected to the register write inputs, the direct d-th outputs of which are connected to the first group of inputs of decoders 70 d and to the first inputs of AND elements 
, the first inverse outputs of the registers 69 f are connected to the first inputs of the second group of inputs of the decoder 70 1, the remaining inverse outputs of the registers 69 f are connected to the inputs of the second group of inputs of the decoder 70 d and to the first groups of inputs of the (D-1) analysis nodes 72 d, the group of outputs of the first decoder 70 1 is connected to the second group of inputs of the analysis node 72 1, the groups of outputs of the remaining decoders 70 d are connected to the third groups of inputs of the analysis nodes 72 d, d-x outputs analysis nodes 72d are connected to the second group of inputs of the (d+1) analysis nodes 72 d+1 The outputs of the (D-1) analysis node are connected to the second inputs of the AND elements 
, the outputs of elements AND 71 1 f -71 D f are connected to the inputs of elements OR 73 f, the outputs of which are connected to the outputs 23 f of the second block for issuing the maximum code 22. In the decoders 64d of the first block for determining the maximum code 18 inputs 
and with the inputs of the first AND elements, the outputs of which are connected to the second inputs of the corresponding OR elements 
, the inputs of the second group of inputs are connected to the inputs of the second AND elements, the outputs of which are connected to the third inputs of the corresponding OR elements 
, the outputs of which are connected to the outputs 
decoders 64 d, . In the analysis nodes 66 d, the first block for determining the maximum code 18 inputs 
the first group, the outputs of which are connected to the corresponding q-and inputs of the OR elements 81 l of the second group, the outputs of which are connected to the first inputs of the corresponding AND elements 
the second group and with the inputs of the corresponding elements NOT 84 d l of the first group, the outputs of which are connected to the first inputs of the AND elements 
the third group, respectively, the outputs of which are connected by the first inputs of the OR elements 
d-th analysis node 66 d, inputs of the second group of inputs 
connected to the second inputs of the AND elements of the first group, to the second inputs of the AND elements 
first group, entrances 
connected to the second inputs of the AND elements 
the second group, the outputs of which are connected to the second inputs of the OR elements 
third group. In decoders 70 d the second device for determining the maximum code 22 inputs 
the first group of inputs are connected to the first inputs of the corresponding OR elements 
and with the inputs of the first AND 94 d elements whose outputs are connected to the second inputs of the corresponding OR elements 
inputs 
the second group of inputs are connected to the inputs of the second AND elements, the outputs of which are connected to the third inputs of the corresponding OR elements 
, the outputs of which are connected to the outputs 
decoders 70 d. In the analysis nodes 72 d of the second device for determining the maximum code there are 22 inputs 
the first group of inputs are connected to the first inputs of the corresponding AND elements 
the first group, the outputs of which are connected to the first inputs of the corresponding OR elements 
the first group, the outputs of which are connected to the corresponding q-and inputs of the OR elements 100 f of the second group, the outputs of which are connected to the first inputs of the corresponding AND elements 
of the second group and with the inputs of the corresponding NOT elements 
the first group, the outputs of which are connected to the first inputs of the AND elements 
the third group, respectively, the outputs of which are connected to the first inputs of the OR elements 
the third group, the outputs of which are connected to the first inputs of the AND elements 
fourth group, the outputs of which are connected to the outputs 
d-th analysis node 72 d, inputs of the second group of inputs 
connected to the second inputs of the AND elements 
first group, with second inputs of AND elements 
fourth group and with inputs of NOT elements 
the second group, the outputs of which are connected to the second inputs of the OR elements 
first group, entrances 
third group of analysis node inputs 
connected to the second inputs of the AND elements 
third group and with inputs of NOT elements 
the third group, the outputs of which are connected to the second inputs of the AND elements 
the second group, the outputs of which are connected to the second inputs of the OR elements 
third group. The purpose of a fuzzy probabilistic automaton is to generate state signals and output signals, as well as generate fuzzy variables defined on sets of states and outputs. The formal mathematical model of a fuzzy probabilistic automaton has the form: , (,T(),Z),(,T(),Y) , where X, Y, Z are sets of input, output parameters and state parameters; 
- a set of conditional probabilities that determine the presence of a probabilistic automaton in a time step t in the state z t, provided that the parameter x t is supplied to the input in this step and the presence of the probabilistic automaton in the previous time step t-1 in the state z t-1; 
- a set of conditional probabilities that determine the presence of the parameter y t at the output of the probabilistic automaton at the time step t, provided that the parameter x t is supplied to the input at this time step and the fuzzy probabilistic automaton is in the state z t in the previous step; (,T(),Z) - specification of a linguistic variable, where - the name of the fuzzy variable "state selection", T () - term set of the linguistic variable, Z - base set; (,T(),Y) - specifying a linguistic variable, where is the name of the linguistic variable “selection of output signal”, T () is the term set of the linguistic variable, Y is the base set. For example, let , where the variables: 1 - “selection of the best states”, 2 - “selection of good states”, 3 - “selection of bad states”, are specified in triplets 
- fuzzy subsets on the base set Z; 1 - "selection of the best output signal", 2 - "selection of a good output signal", 3 - "selection of a bad output signal" are specified by the set 
- fuzzy sets defined on the base set Y. Membership functions are specified based on a survey of experts. When preparing a fuzzy probabilistic automaton for operation, the following operations should be performed. By setting inputs they are written to registers 
(Fig. 1 and 3) first memory block 2 codes of the given matrices of transition probabilities 
. Using the setting inputs, the codes of the probability matrices for selecting the output signal are written into registers 30 m i p (Fig. 1 and 4) of the second memory block 3 
. According to the installation inputs 17 1 1 i -17 D l i of the fourth memory block 16 are written to registers 
(Fig. 1 and 12) values of degrees of membership of fuzzy variables 1. . By installation inputs 
the values of degrees of membership of fuzzy variables f are written into registers 68 fp of the fifth memory block 20. . Matrices 
have the form: 
Where
P m i j is the probability that when a signal x m arrives at time t, the machine will go into state z j, provided that at time t-1 it was in state z i. Reduced matrices 
have the form: 
,
Where
Probability matrices 
are given in the following form: 
,
Where
P m i p is the probability that when a signal x m arrives at time t, the machine will generate a control action y p, provided that at time t-1 it was in state z i. Reduced matrices 
are given in the following form: 
,
Where
When writing codes to registers 24, the probability of the matrix P m z will be written to the K-bit register 24 m i j of memory block 2, and the probability of the matrix P m y will be written to the K-bit register 31 m i j of memory block 3. Information about the membership function is entered according to the following rule . The power of the set is , and the range (0,1) of the values of the membership functions is quantized (in Fig. 20, quantization is shown in seven levels). For each state z i there are L values of membership functions
. For the example under consideration, L = 3. Codes will be written to registers 62 l1 - 62 lN of the fourth memory block 26. Similar reasoning is valid for writing quantized values of membership functions. Codes will be entered into registers 68 f1 - 68 fp of the fifth memory block 20. A fuzzy probabilistic automaton operates according to the following algorithm. Synchronization of the fuzzy probabilistic automaton is carried out by a generator of 13 clock pulses. Inputs 1 1 - 1 M supply input signals x t that control the operation of the fuzzy probabilistic automaton. The third memory block stores the state of the machine. When a control action x m is received at input 1 m at time t, depending on what state z i the machine was in at time t-1, i.e., depending on the signal at output 8 i coming from the third memory block 7 to input 8 i of memory block 2 and input 8 i of memory block 3, codes i-q of the matrix row are supplied to the outputs of memory block 2, and codes are supplied to the outputs of the second memory block 3 i-th line matrices This happens as follows. Since in block 2 there is potential at inputs 8 i, 2 m, as well as at input 26, the AND elements (25 1m il -25 Km i1)(25 1m iN -25 Km iN) and register codes 24 i1 - will be open 24 iN through these AND elements and OR elements 28 will be supplied to the groups of outputs (29 1 1 -29 K l)(29 1 N -29 K N) respectively. In the same way, in the second memory block 3, the probability codes of registers 30 i1 - 30 ip through open AND elements (31 1m il -31 Km i1)(31 1m iP -31 Km iP) and OR elements 33 will be supplied to output groups (34 1 1 -34 K 1)(34 1 P -34 K P). . The first 14 and second 15 random code generation blocks generate number codes uniformly distributed over the interval (0,1). State selection block 6, in accordance with the test rule in the random event scheme, generates the current state z t . Also in block 10 for selecting the output signal, in accordance with the test rule in the random event circuit, the output signal y t is generated. The signals z t and y t determined for time t are supplied to inputs 8 of the switch 9 and inputs 11 of the switch 12, respectively. Depending on the received signal z i at time t, from the outputs of the first switch 9 the values of the degrees of membership of fuzzy variables corresponding to the signal z i are supplied to the block for determining the maximum code. Block 18 for determining the maximum code analyzes the values of the code combinations received at its input, and output 19 l receives a signal whose index l corresponds to the largest value of the degree of membership of variable 1 . . When, at time t, the output signal y P is received at the input 11 p of the second switch 12, the outputs of the switch 12 receive the values of the degrees of membership of the fuzzy variables for the element y p of the base set Y. Next, the maximum code determination block analyzes the received code combinations, and one of the f outputs a single signal corresponding to the largest code combination is received. Let us consider the operation of a fuzzy probabilistic automaton in more detail. Let, for example, it is known that the set of states has three elements Z = (z 1, z 2, z 3), the set of output signals also has three elements Y = (y 1, y 2, y 3), and let at the moment of time t control signal x 2 is applied to input 12. Let the transition probability matrix have the form: 
Registers 
are intended for storing K = 8-bit values of probability values. Let at the moment of time (t-1) the machine was in state z 1, therefore, a single signal was received from input 8 1, which made it possible to read the contents of the first row of the matrix when a synchronizing signal was received from the clock pulse generator 13 at input 26 from registers 24 2 1 1 -24 2 3 1 through elements AND 25 12 11 -25 82 11 25 12 33 -25 82 33, OR 28 to outputs 29 1 1 -29 8 1 -29 1 3 -29 8 3. . That is, at outputs 29 1 1 -29 8 1 there will be a binary code of the number 0, 1, at outputs 29 1 2 -29 8 2 - a binary code of the number 0, 4, and at outputs 29 1 3 -29 8 3 - a binary code number code 1. The circuit implementation of the second memory block 3 is identical to the circuit implementation of the first memory block 2. The operation of block 3 will proceed in the same way as the operation of block 2. The first random code generation block 14 operates as follows. Random pulses from the generator 55 of the Poisson pulse stream arrive through the open (at time intervals corresponding to the machine being in i-th states ) the first element AND 52 to the synchronizing input of the cyclically closed shift register 56, in one of the bits of which a unit is written, and the rest are zeros. The intensity of the random pulses of the generator 55 significantly exceeds the polling frequency at input 51. Then the recorded unit repeatedly “runs around” the shift register 56 between the moments of polling its states at the input 51 pulses of the clock pulse generator 13. Under this condition, one will be at the time of polling at any of the outputs of the shift register 56 with a probability equal to one divided by the number of outputs of register 56. The code converter converts the code for one combination into the binary code of a number equally likely distributed over the interval (0,1). The second random code generation block 15 works in a similar way. In state selection block 6 (Fig. 5), each i-th comparison node 35 i analyzes the code combination received from inputs 29 1 i -29 K i of the first group of inputs and the code combination, received from the random code generation unit at inputs 36 1 - 36 K of the second group of inputs. Comparison nodes function similarly to those given in (Design of microelectronic digital devices / Edited by S.A. Mayorov. - M.: Sov. Radio, 1977, pp. 127 - 134). If the value of the code combination arriving through inputs 36 1 - 36 K is less than or equal to the value arriving through the i-th group of inputs 29 1 i -29 K i to the i-th comparison node, then to the inputs of elements I38 i-1 corresponding to the comparison nodes i, and for the first element I38 1, a single signal is received at the output 37 1 of the state selection block 6, and a zero signal is received at subsequent elements 38 g, closing these elements. Thus, the state selection block 6 determines the state z i into which the fuzzy probabilistic automaton goes at time t. Let us assume that in our case a single signal arrived at output 37 3, and this means that the machine switched to state z 3 at time t. The third memory block (see Fig.6) delays a single signal z i received at input 37 i from the state selection unit 6 for one clock cycle of the generator 13, and then outputs it to output 8 i . It goes like this. A single signal applied to input 37 3 throws trigger 38 3 into the single state. The potential from the single output of the trigger 38 3 resets the triggers 38 1, 38 2 to the zero state through the OR elements 39 1, 39 2 and is supplied to the output 8 3 of the fuzzy probabilistic automaton and the input 8 3 of the switch 9. The output selection block functions similarly to the state selection block 6 signal 10. The output signal Y p determined by block 10 is supplied to output 11 p of the fuzzy automaton and input 11 p of the second switch 12. When the signal z i , , is received at time t c of output 8 i of the third memory block 7, the L D-bit values of the membership functions are read from the registers first memory block 6. The potential at output 8 i will open the AND elements 
. The values of the contents of registers 62 li are read, which from the outputs of the switch 9 is supplied to the inputs of the first block 18 for determining the maximum code in the form of L groups of D-bit codes of the values of the membership function of fuzzy variables 1 at point z i . When a signal Y p is received from the output signal selection block 10 at time t, the F D-bit values of the membership functions are read from the registers of the second memory block 20. The potential at the output 11 p will open the AND elements 
. The contents of the registers 68 fr, through the switch 12, are supplied to the inputs of the second block 22 for determining the maximum code in the form of F groups of D-bit codes of the values of the membership function of fuzzy variables f at point Y p. Block 18 for determining the maximum code analyzes 9 L D-bit code combinations coming from the switch, which are, respectively, degrees of membership of fuzzy variables, i.e. establishes which of the fuzzy variables has a greater value of the membership function for the current state, and sends a signal to the output about the number of the largest code combination. L code combinations are supplied to the input buses 43 1 - 43 L (Fig. 13), from which the maximum code determination device must select the maximum code combination, and if there are k codes of equal size in the inputs 43 1 - 43 L and maximum among L code combinations, then such a case should also be recognized. Each 1st code combination is supplied via input buses 43 1 1 -43 d L to the corresponding register 63 l. Code combinations are written to register cells 63 1 - 63 L parallel in time, but sequentially in digits. First, pulses will be sent to input buses 43 1 1 ,43 1 2 ,43 1 3 ,...,43 1 L , then to input buses 43 2 1 ,43 2 2 ,43 2 3 ,...,43 2 L , etc. until the final supply of pulses of code combinations along the input buses 43 D 1,43 D 2,43 D 3,...,43 D L,. Parallel-sequential recording of code combinations in registers 63 ensures sequential operation in time of state decoders 64 and analysis nodes 66. The operating algorithm of the maximum code determination block consists of sequential analysis of parallel (of the same name) bits of code combinations written in registers 63 1 - 63 L with sequential identification large codes in parallel (of the same name) bits, starting from the most significant bit down to the least significant one. Moreover, the analysis of parallel bits of code combinations of registers 63 is carried out both by state decoders 64 and analysis nodes 66. Identification of code combinations larger in value than the smallest is carried out by the first state decoder 64 1 and analysis nodes 66 1 - 66 D-1, the latter analysis node 66 D-1 identifies the maximum (one or more) code combinations from N, recorded in registers 63. The essence of the algorithm for the operation of the maximum code determination unit is as follows. First, let's look at the parallel high-order bits a 1 1 -a 1 L of registers 63. Obviously, the following events are possible here. The symbols of all bits a 1 1 -a 1 L are equal to zero, the symbols of all bits a 1 1 -a 1 L are equal to one, or there are symbols equal to zero and one. In the first two cases, there should be unit potentials at the outputs 79 1 1 -79 1 L of the decoder 64 1, and in the third case, unit potentials should be at those outputs 79 1 1 -79 1 L that correspond to the subscript registers 63 in the high order the cells of which a 1 1 -a 1 L contain single values of code bits, i.e. the logical function that determines the signal at the 1st output 79 1 l of the first decoder 64 1 can be written in the following form:
. To determine the signal on lth output d-th decoder 64 d, based on the method of mathematical induction, we can write the following logical function
. Equality is a sufficient condition, but not necessary to determine that in register 63 l there can be maximum number, i.e. decoders 64 d allocate registers 63 l in which the symbols a l are equal to one. The first determining state of the l-th output 88 d l d of the analysis node 66 d is the event: what is the state of the l-th output 88 d l -1 (d-1) of the analysis node 66 d-1 , and for the first analysis node 66 1 the state of the l-th output 88 1 l is determined by the state of the l-th output 79 1 l of the first decoder 64 1 . The second determining state of the l-th output 88 d l d of the analysis node 66 d is the event determined by the inversion of the equivalence of two statements d l and some logical function d l, which is determined by the expression: 
Moreover 
is always equal to zero if G d l -1 or , or one of the (L-1) disjunctions included in the conjunctive normal form (2) are equal to zero. Function 
defining the state of the l-th output of the d-th analysis node 66 d (one or zero at the output 88 d l), written in the form:
From equations (1), (2) and (3) it follows that 
is always equal to zero if either d l, or G d 1, or G d 2, etc. to G d 1 -1 are equal to zero. From the outputs of the analysis unit 66 D-1, the code combination G D l -1 is received, and each output 88 D l -1 is connected to the second group of inputs of elements AND 65 1 l -65 D l .. The unit potential at the output 88 D l -1 allows open the group of elements AND 65 1 l -65 D l to which the maximum code came from register 63 l. Then the maximum code combination is supplied to the input of OR elements 67 1 l -67 D l , after which the signal about the maximum code index appears at one of the outputs 19 1 - 19 L of the first block for issuing the maximum code 18. Thus, the value of the fuzzy variable that has the greatest the value of the degree of membership in a given state. The second maximum code determination unit 22 operates in the same way as the first maximum code determination unit 18, so its operation will not be described in detail. So, at the outputs 19 l of the first block for determining the maximum code 18, a potential will be fixed that determines the index l of the fuzzy variable 1, the most preferable for the current state. At the outputs 23 f of the second block for determining the maximum code 22 there will be a potential that determines the index f of the fuzzy variable f, the most preferable for the current state. The technical and economic efficiency of the proposed device in relation to the known one (AS USSR N 1200297, class G 06 F 15/20, 1985) can be determined from the expansion of functionality, namely, the proposed device generates not only states, output signals, but also linguistic variables defined on basic sets of states and output signals. The membership functions of fuzzy variables are specified by the expert survey method. The functions of transitions and outputs of the machine are specified in the form of randomized rules. If we estimate the costs of developing and manufacturing the proposed device through the value C 1, the costs of conducting research through the value C 2, then we will determine the total costs of solving the problem
CI = C 1 + C 2. When using a known device to solve control problems, costs are required for the manufacture of special additional devices and conducting full-scale experiments. We determine these costs by the value CN. Note that the costs of CN will significantly exceed the value of CI, since carrying out full-scale tests already requires significant economic costs.
Claim
1. A fuzzy probabilistic automaton containing a clock pulse generator, a first random code generation block, a state selection block, an output signal selection block, first, second and third memory blocks and a switch, and M inputs of the group of control inputs of the device are connected to M inputs of the first control groups inputs of the first memory block, the inputs (N x N x M) of the groups of the first installation inputs of the device are connected, respectively, to the inputs of the N x N x M groups of installation inputs of the first memory block, the N inputs of the groups of second control inputs of which are connected to the N outputs of the group of outputs of the third memory block , a group of information outputs of the first memory block is connected to the inputs of the first group of information inputs of the state selection block, the output of the clock pulse generator is with the clock input of the first random code generation block, K outputs of the group of outputs of which are connected to the K inputs of the second group of information inputs of the state selection block, different in that it additionally includes a second random code generation block, a fourth and fifth memory block, a second switch, a first and second maximum code determination block, and the N x P x M inputs of the installation input groups of the second memory block are connected to the N x P x inputs M groups of second installation inputs of the device, M inputs of the group of first control inputs are connected to M inputs of the group of control inputs of the device and M inputs of the group of first control inputs of the first memory block, N inputs of the group of second control inputs are connected to N inputs of the group of second control inputs of the first memory block, N outputs of the group of outputs of the third memory block and N inputs of the group of control inputs of the first switch, outputs P of groups of information outputs of the second memory block are connected to the corresponding inputs of P groups of information inputs of the output signal selection block, and the clock input of the second memory block is connected to the output of the clock pulse generator and clock inputs of the first memory block, the second random code generation block, N outputs of the group of information outputs of the state selection block are connected to the corresponding N inputs of the group of first information inputs of the third memory block, K outputs of the group of outputs of the second random code generation block are connected to K inputs of the group of second information inputs output signal selection block, inputs of N x L groups of information inputs of the first switch are connected to outputs of N x L groups of information outputs of the fourth memory block, N x L groups of information inputs of which are connected to inputs of N x L third groups of installation inputs of the device, outputs of L groups of information the outputs of the first switch are connected to the inputs of L groups of information inputs of the first block for determining the maximum code, the outputs of the group of information outputs of which are connected to the outputs of the third group of outputs of the device, P outputs of the group of outputs of the output signal selection block are connected to P inputs of the group of control inputs of the second switch, inputs P x F groups of information inputs of which are connected to outputs P x F groups of information outputs of the fifth memory block, inputs P x F groups of information inputs of which are connected to inputs P x F of the fourth groups of installation inputs of the device, outputs P groups of information outputs of the second switch are connected to inputs of F groups information inputs of the second block for determining the maximum code, the groups of information outputs of which are connected to the outputs of the fourth group of device outputs. 2. The machine according to claim 1, characterized in that the first memory block contains registers, N x M groups of AND elements, N x M groups of OR elements, and each of the k inputs (i, j, m) 
groups of setting inputs are connected to the recording inputs of the corresponding (i, j, m) registers, the outputs of which are connected to the first inputs of the corresponding elements of the (i, j, m) groups of AND elements, the second inputs of the AND elements are combined and connected to the clock input memory block, the third inputs of the elements AND of each of the m groups are combined and connected to the m-th inputs of the group of the first control inputs of the block, the fourth inputs of the elements AND (im)-th group are combined and connected to the i-th input of the second group of control inputs of the block, outputs AND elements - with corresponding inputs of N x M groups of OR elements, the outputs of which are connected, respectively, to the outputs of N groups of block outputs. 3. The machine according to claim 1, characterized in that the state selection block contains N comparison nodes, N - 1 AND elements, and k inputs j of the first group of information inputs connections with the inputs of the first groups of inputs j-th nodes comparisons, the same inputs of the second groups of inputs of which are combined and connected to the corresponding k inputs of the second group of information inputs of the block, the output of the first comparison node is connected to the first output of the block and to the first inverse inputs of the AND elements, the outputs of the i-th comparison nodes are connected to the direct inputs of the corresponding ( i - 1)-th elements AND and with i-th inverse inputs i-th elements And, the outputs of which are connected to the (i + 1) outputs of the block. 4. The machine according to claim 1, characterized in that the third memory block contains N flip-flops and N OR elements, and its inputs are connected to the single inputs of the corresponding triggers, the zero inputs of which are connected to the outputs of the corresponding OR elements, and the single outputs are connected to the outputs of the block and the corresponding inputs of the corresponding OR elements, and the single output of the i-th trigger is connected to i-th output block and with the corresponding inputs (1 - (i - 1) - (i + 1) - N) OR elements. 5. The machine according to claim 1, characterized in that the output signal selection block contains P comparison nodes and P - 1 AND elements, and the k inputs of the p first groups of information inputs are connected to the inputs of the first groups of inputs of the p-th comparison nodes, the same inputs of the second groups of inputs of which are combined and connected to the corresponding k inputs of the second group of information inputs of the block, the output of the first comparison node is connected to the first output of the block and to the first inverse inputs of the AND elements, p-x outputs comparison nodes are connected to the direct inputs of the corresponding (p - 1)-th AND elements and to the p-th inverse inputs of the p-th AND elements, the outputs of which are connected to the (p + 1)-th outputs of the block. 6. The machine according to claim 1, characterized in that the first random code generation block contains the first and a group of second AND elements, a code converter, and the clock input is connected to the inverse input of the first AND element and to the first inputs of the group of second AND elements, the outputs of which are connected to by the corresponding inputs of the code converter, the outputs of which are connected to the outputs of the block, the output of the Poisson pulse stream generator is connected to the direct input of the first AND element, the output of which is connected to the clock input of a cyclically closed shift register, the bit outputs of which are connected to the second inputs of the corresponding second elements of the AND group. 7. The machine according to claim 1, characterized in that the first block for determining the maximum code contains L registers, D decoders, D - 1 analysis nodes, L groups of D AND elements and a group of L OR elements, and l-th a group of inputs are connected to the recording inputs of the l-th registers, the direct d-th outputs of which are connected to the first group of inputs of the d-th decoders and to the first inputs d-th elements AND l-th group, first inverse l-x outputs registers are connected to the first inputs of the second group of inputs of the l-th decoders, the remaining inverse outputs of the l-th registers are connected to the inputs of the second group of inputs of the d-th decoders,
The invention relates to information-measuring technology and is intended to simultaneously obtain a pair of probabilistic characteristics representing a two-dimensional histogram of the duration of exceedances by outliers and failures of various durations of different levels of analysis
The invention relates to information, measuring and computing technology, is intended to obtain a two-dimensional histogram of the voltage level and derivative and can be used in the electric power industry to assess voltage variability in industrial electrical networks, as well as in other fields of technology, for example, to study and evaluate the behavior of various swinging objects: ship decks, tank platforms during movement, etc.
The invention relates to computer technology and control systems and can be used to construct adaptive fuzzy controllers for solving problems of controlling objects, the mathematical model of which is not a priori defined, and the purpose of operation is expressed in fuzzy concepts
