Development of neural­network and fuzzy models of multimass electromechanical systems

Authors

DOI:

https://doi.org/10.15587/1729-4061.2019.169080

Keywords:

identification of multimass systems, neural-network models, fuzzy approximating systems, hybrid networks.

Abstract

The study objective was to construct models of multimass electromechanical systems using neural nets, fuzzy inference systems and hybrid networks by means of MATLAB tools. A model of a system in a form of a neural net or a neuro-fuzzy inference system was constructed on the basis of known input signals and signals measured at the system output. Methods of the theory of artificial neural nets and methods of the fuzzy modeling technology were used in the study.

A neural net for solving the problem of identification of the electromechanical systems with complex kinematic connections was synthesized using the Neural Network Toolbox application package of the MATLAB system. A possibility of solving the identification problem using an approximating fuzzy system using the Fuzzy Logic Toolbox package was considered. A hybrid network was synthesized and implemented in a form of an adaptive neuro-fuzzy inference system using the ANFIS editor. Recommendations for choosing parameters that have the most significant effect on identification accuracy when applying the methods under consideration were given. It was shown that the use of neural nets and adaptive neuro-fuzzy inference systems makes it possible to identify systems with accuracy of 2 to 4%.

As a result of the conducted studies, efficiency of application of neural nets, fuzzy inference systems and hybrid nets to identification of systems with complex kinematic connections in the presence of "input-output" information was shown. The neural-network, fuzzy and neuro-fuzzy models of two-mass electromechanical systems were synthesized with the use of modern software tools.

The considered approach to using artificial intelligence technologies, that is neural nets and fuzzy logic is a promising line of construction of appropriate neural-network and neuro-fuzzy models of technical objects and systems. The study results can be used in synthesis of regulators for the systems with complex kinematic connections to ensure their high performance.

Author Biographies

Gennady Kaniuk, Ukrainian Engineering Pedagogics Academy Universytetska str., 16, Kharkiv, Ukraine, 61003

Doctor of Technical Sciences, Professor

Department of Heat Power Engineering and Energy Saving Technologies

 

Tetiana Vasylets, Ukrainian Engineering Pedagogics Academy Universytetska str., 16, Kharkiv, Ukraine, 61003

PhD, Associate Professor

Department of Heat Power Engineering and Energy Saving Technologies

Oleksiy Varfolomiyev, DeVry University Madison ave., 180, New York, USA, 10016

PhD

Andrey Mezerya, Ukrainian Engineering Pedagogics Academy Universytetska str., 16, Kharkiv, Ukraine, 61003

PhD, Associate Professor

Department of Heat Power Engineering and Energy Saving Technologies

Nataliia Antonenko, Ukrainian Engineering Pedagogics Academy Universytetska str., 16, Kharkiv, Ukraine, 61003

PhD, Associate Professor

Department of Heat Power Engineering and Energy Saving Technologies

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Published

2019-05-29

How to Cite

Kaniuk, G., Vasylets, T., Varfolomiyev, O., Mezerya, A., & Antonenko, N. (2019). Development of neural­network and fuzzy models of multimass electromechanical systems. Eastern-European Journal of Enterprise Technologies, 3(2 (99), 51–63. https://doi.org/10.15587/1729-4061.2019.169080

Issue

Vol. 3 No. 2 (99) (2019): Information technology. Industry control systems

Section

Industry control systems

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Copyright (c) 2019 Gennady Kaniuk, Tetiana Vasylets, Oleksiy Varfolomiyev, Andrey Mezerya, Nataliia Antonenko

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