SYNTHESIS OF NEURAL NETWORK MODELS FOR TECHNICAL DIAGNOSTICS OF NONLINEAR SYSTEMS

Authors

  • S. D. Leoshchenko National University “Zaporizhzhia Polytechnic”, Zaporizhzhia, Ukraine, Ukraine
  • A. O. Oliinyk National University “Zaporizhzhia Polytechnic”,Zaporizhzhia,Ukraine, Ukraine
  • S. A. Subbotin National University “Zaporizhzhia Polytechnic”, Zaporizhzhia, Ukraine, Ukraine
  • B. V. Morklyanyk Military Academy, Kyiv, Ukraine, United Kingdom

DOI:

https://doi.org/10.15588/1607-3274-2025-2-11

Keywords:

technical diagnostics, nonlinear systems, machine learning, neural network synthesis, indicator system, neuromodel, sampling, learning, error

Abstract

Context. The problem of synthesizing a diagnostic model of complex technical processes in nonlinear systems, which should be characterized by a high level of accuracy, is considered. The object of research is the process of synthesizing a neural network model for technical diagnostics of nonlinear systems.
Objective of the work is to synthesize a high-precision neural network model based on previously accumulated historical data about the system.
Method. It is proposed to use artificial neural networks for modeling nonlinear technical systems. First, you need to perform an overall assessment of the complexity of the task. Based on the assessment, a decision can be made on the best approach to organizing neuromodel synthesis. So, for the task, the level of ‘random complexity’ was chosen, because despite the relative structure of the data, their total array is quite large in volume and requires careful study in order to ensure high quality of the solution. Therefore, in the future, it was proposed to use a neuromodel based on recurrent networks of the GRU topology and use swarm intelligence methods for neurosynthesis, in particular the A3C method. The results obtained showed a high level of solution obtained, but due to the high level of resource intensity, the proposed approach requires further modifications.
Results. A diagnostic model of complex technical processes in nonlinear systems of optimal topology, characterized by a high level of accuracy, is obtained. The built neuromodel reduces the risks associated with ensuring human safety.
Conclusions. The conducted experiments confirmed the operability of the proposed approach and allow us to recommend it for further refinement in order to implement technical, industrial and operational process control systems in practice in automation systems. Prospects for further research may lie in optimizing the resource intensity of synthesis processes

Author Biographies

S. D. Leoshchenko, National University “Zaporizhzhia Polytechnic”, Zaporizhzhia, Ukraine

PhD, Associate Professor of the Department of Software Tools

A. O. Oliinyk, National University “Zaporizhzhia Polytechnic”,Zaporizhzhia,Ukraine

Dr. Sc., Professor, Professor of the Department of Software Tools

S. A. Subbotin, National University “Zaporizhzhia Polytechnic”, Zaporizhzhia, Ukraine

Dr. Sc., Professor, Head of the Department of Software Tools

B. V. Morklyanyk, Military Academy, Kyiv, Ukraine

Dr. Sc., Professor, Professor of the Department of Information Technology

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Published

2025-06-29

How to Cite

Leoshchenko, S. D., Oliinyk, A. O., Subbotin, S. A. ., & Morklyanyk, B. V. . (2025). SYNTHESIS OF NEURAL NETWORK MODELS FOR TECHNICAL DIAGNOSTICS OF NONLINEAR SYSTEMS. Radio Electronics, Computer Science, Control, (2), 126–132. https://doi.org/10.15588/1607-3274-2025-2-11

Issue

Section

Neuroinformatics and intelligent systems