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Models of dynamic objects with significant nonlinearity based on time-delay neural networks

Oleksandr Fomin, Viktor Speranskyy, Valentyn Krykun, Oleksii Tataryn, Vladyslav Litynskyi

Received 15.05.2023, Revised 21.08.2023, Accepted 18.09.2023

Abstract

The paper is devoted to the problem of nonlinear modeling of objects based on dynamic neural networks. The aim of the work is to improve the accuracy of modeling dynamic objects with significant nonlinearities using neural network models and to determine the scope of effective application of these models. This aim is achieved using time-delay neural networks. To assess the applicability of the proposed neural network models, the study considers simulation objects with two types of nonlinearities: smooth and piecewise linear (saturation). The investigation of suggested models accuracy in nonlinear dynamic object modeling involves two experiments: the study of the models' scalability with different input signals; the study of their extrapolation capabilities. The results of both experiments are compared with the modeling results using the compensatory method of deterministic identification based on functional series. The results of the experiments reveal that the suggested neural network models are not invariant concerning the input signal. However, when trained on a sufficient amount of data generated from input signals of the same type as in the test data set, these models can effectively represent the properties of nonlinear dynamic objects. The extrapolation properties of timedelayed neural networks deteriorate as the input signal amplitudes exceed the range covered by the used training set. The scientific novelty consists in determining a clear relationship between the types of input signals, their amplitudes, and the accuracy of the proposed models. The practical significance of investigation delineates the areas in which time-delay neural networks can be used to address the realworld challenges associated with significantly non-linear objects; demonstrates the increase in accuracy of identifying nonlinear objects compared to functional series models

Keywords:

identification; nonlinear objects; substantial nonlinearities; dynamic neural networks; simulation modeling

https://doi.org/10.24025/2306-4412.3.2023.288284

Retrieved from Volume 28, No. 3, 2023

Pages 97-112

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Suggested citation

Fomin, O., Speranskyy, V., Krykun, V., Tataryn, O., & Litynskyi, V. (2023). Models of dynamic objects with significant nonlinearity based on time-delay neural networks . Bulletin of Cherkasy State Technological University, 28(3), 97-112. https://doi.org/10.24025/2306-4412.3.2023.288284