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A New Approach to Nonlinear Modeling Based on Significant Operating Points Detection

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Artificial Intelligence and Soft Computing (ICAISC 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9120))

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Abstract

The paper presents a new approach to nonlinear modeling based on significant operating points detection from non-invasive identification of nonlinear dynamic system. The swarm intelligence supported by the genetic algorithm is used in the proposed approach to identify the unknown parameters of the nonlinear dynamic system in different significant operating points. The parameters of the membership functions of the fuzzy rules and the parameters of the linear models are simultaneously identified. The new approach was tested on the nonlinear electrical circuit, which was replaced by the approximate linear model. The obtained results prove efficiency of the new approach based on the significant operating points detection.

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Author information

Authors and Affiliations

  1. Institute of Computational Intelligence, Czȩstochowa University of Technology, Czȩstochowa, Poland

    Piotr Dziwiński

  2. Moscow Institute of Physics and Technology, Moscow, Russia

    Eduard D. Avedyan

Authors
  1. Piotr Dziwiński
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  2. Eduard D. Avedyan
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Correspondence to Piotr Dziwiński .

Editor information

Editors and Affiliations

  1. Czestochowa University of Technology, Czestochowa, Poland

    Leszek Rutkowski

  2. Czestochowa University of Technology, Czestochowa, Poland

    Marcin Korytkowski

  3. Czestochova Univ of Technology, Częstochova, Poland

    Rafal Scherer

  4. AGH University of Science and Technology, Krakow, Poland

    Ryszard Tadeusiewicz

  5. University of California, Berkeley, California, USA

    Lotfi A. Zadeh

  6. Louisville, Kentucky, USA

    Jacek M. Zurada

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Dziwiński, P., Avedyan, E.D. (2015). A New Approach to Nonlinear Modeling Based on Significant Operating Points Detection. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2015. Lecture Notes in Computer Science(), vol 9120. Springer, Cham. https://doi.org/10.1007/978-3-319-19369-4_33

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  • DOI: https://doi.org/10.1007/978-3-319-19369-4_33

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19368-7

  • Online ISBN: 978-3-319-19369-4

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