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Instruments and Systems: Monitoring, Control, and Diagnostics

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Development of Identifi cation Methodology and Modeling of Dynamic Systems Based on Neural Networks in Matlab Environment
B.M. TEMERBEKOVA, U.B. MAMANAZAROV, B.M. BEKIMBETOV

The purpose of this study is to develop a methodology for identifi cation and modeling of dynamic systems using neural networks in
MATLAB. As part of the work, a neural network has been developed and trained to accurately predict the responses of the system. The
methodology includes data normalization, model training, prediction and transfer function identifi cation. The results obtained demonstrate
the high accuracy of the model: the mean square error (MSE) was 0.021182 and the coeffi cient of determination (R²) reached 0.99939.
The identifi ed transfer function of the system also showed a high degree of agreement with the data, confi rming its adequacy for modeling
dynamic processes. The methodology proved to be effective and can be used in various technical and industrial applications to improve
control systems.
Keywords: neural networks, MATLAB, system identifi cation, modeling, transfer function, dynamic system.


DOI: 10.25791/pribor.10.2024.1532

Pp. 53-62.

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