METHOD FOR CONSTRUCTING SIMILARITY CRITERIA FOR STOCHASTIC STATIONARY MATERIAL FLOWS
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Abstract
The object of the study is a stationary stochastic input flow of material arriving at the input of a transport conveyor system. The goal of the study is to develop methods for comparing such flows based on dimensionless similarity criteria that allow classifying flows with different statistical characteristics but similar structural properties. The results obtained. The study proposed a canonical representation of the input stochastic flow in the form of a Fourier decomposition over a fixed time interval. Based on this representation, a technique was developed for constructing aggregated similarity criteria that take into account the mathematical expectation, standard deviation, and correlation time of the input flow. The applicability of the proposed criteria for modeling and analyzing stochastic processes in transport systems is substantiated. Two alternative methods of dimensionless representation of the flow model are introduced, each of which allows unifying the description of input implementations. A multilayer perceptron trained on the basis of experimental data is used to identify the distribution law of random components of the canonical decomposition. A comparative analysis of real and synthetic implementations of the input flow was carried out, confirming the effectiveness of the proposed approach in the task of reproducing the statistical and correlation characteristics. Conclusion. The developed technique allows for the classification and comparison of input material flows in transport systems, and also serves as a basis for creating a universal approach to constructing mathematical models and flow control algorithms under stochastic uncertainty.
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