Modeling the parallelism of empirical models of optimal complexity using a Petri net

Gorbiychuk, Mikhail; Bila, Olga; Humeniuk, Taras

doi:10.15587/1729-4061.2019.171632

Cited by 1 publication

(2 citation statements)

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“…Such a complication occurs due to an increase in the power of polynomial or the addition of a new one term of the series, leading to an increase in the volume of calculations. To reduce the volume of calculations, it is proposed to consider a method that uses the theory of genetic algorithms in its implementation [12].…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

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Improving empirical models of complex technological objects under conditions of uncertainty

Горбійчук

KROPYVNYTSKYI

Kropyvnytska

2023

EEJET

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This paper proposes a method for improving empirical models of complex technological objects with insufficient information about the input and output values of an object's parameters. It has been established that most methods for constructing empirical models require knowledge of the statistical characteristics of the input and output values of an object. When modeling complex non-reproducible stochastic processes that evolve over time, information about the parameters and structure of an object is usually not available. A method has been proposed where input and output values are treated as fuzzy quantities with a triangular membership function. Since at some points in the region, the triangular membership function is undifferentiated, it is inconvenient to use it in its typical form to solve the problem of optimal control. Therefore, it is proposed to approximate it with the Gaussian membership function. It is shown that such an approximation is reduced to finding one parameter, which is determined by the least squares method. Its value practically does not depend on the magnitude of the uncertainty interval while the value characterizing the accuracy of approximation is a monotonously increasing function that has a linear character. This makes it possible to define the main operations on fuzzy numbers and derive an empirical model for the case of a polynomial "base" model. The resulting model is linear in its parameters, so a genetic algorithm can be used to find them. It is shown that genetic algorithms can be used in the construction of empirical polynomial models when input parameters are interpreted as fuzzy numbers. Thus, it can be argued that when constructing an empirical model of an object that is affected by external disturbances that cannot be measured, it is advisable to approximate all input quantities with a triangular Gaussian membership function

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Section: Literature Review and Problem Statementmentioning

confidence: 99%

“…The main advantage of using genetic algorithms is the absence of the need for additional information. Moreover, it was shown in [12] that this algorithm has internal parallelism. This makes it possible to develop software that will reduce the cost of machine time for calculations.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%