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Simulation Modeling of Rapidly Changing Loads of a Multi-parameter Object on the Basis of Hybrid Neural Networks

  • 2025
  • OriginalPaper
  • Chapter
Published in:
Authors
Volodymyr Zinovkin
Mykola Antonov
Vira Savchenko
Mykhailo Zaluzhnyi
Published in
Sustainable Development in Economics, Technology and Environmental Engineering
Publisher
Springer Nature Switzerland

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Abstract

This chapter explores the simulation modeling of rapidly changing loads in multi-parameter objects, with a focus on electric arc furnaces. The study delves into the challenges of controlling rapidly changing electrical parameters and the inefficiencies that arise from traditional control methods. A predictive simulation model based on hybrid neural networks (HNN) is proposed to address these issues. The model's accuracy is validated through experimental data, demonstrating a modeling error of less than 4%. The chapter also discusses the limitations of the proposed method and suggests areas for further development. Key topics include the analysis of rapidly changing loads, the development of a predictive simulation model using HNN, the validation of the model through experimental data, and the discussion of limitations and future research directions. The conclusion highlights the potential of the predictive simulation model to improve energy efficiency and reliability in electro-technological equipment.
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previous chapter Modeling of Safe Object Detection by Near-Field and Nonlinear Radar Systems
next chapter A Review on IoT-Based Intelligent Modeling for Environmental and Ecological Engineering
Title
Simulation Modeling of Rapidly Changing Loads of a Multi-parameter Object on the Basis of Hybrid Neural Networks
Authors
Volodymyr Zinovkin
Mykola Antonov
Vira Savchenko
Mykhailo Zaluzhnyi
Copyright Year
2025
Book
Sustainable Development in Economics, Technology and Environmental Engineering

Print ISBN: 978-3-031-91952-7

Electronic ISBN: 978-3-031-91953-4

Copyright Year: 2025

DOI
https://doi.org/10.1007/978-3-031-91953-4_41
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