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A Population Based Algorithm and Fuzzy Decision Trees for Nonlinear Modeling

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Artificial Intelligence and Soft Computing (ICAISC 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10842))

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Abstract

The paper presents a new approach for using the fuzzy decision trees for non-linear modeling based on the capabilities of participle swarm optimization and evolutionary algorithms. The most nonlinear dynamic objects have their approximate nonlinear model. Their parameters are known or can be determined by one of the typical identification procedure. The obtained approximate nonlinear model describes well the identified dynamic object only in the operating point. In this work, we use hybrid model composed with of two parts: approximate nonlinear model and fuzzy decision tree. The fuzzy decision tree contains correction values of the parameters in terminal nodes. The hybrid model ensures sufficient accuracy for the practical applications. A participle swarm optimization and evolutionary algorithm were used for identification of the parameters of the approximate nonlinear model and fuzzy decision tree. An important benefit of the proposed method is the obtained characteristics of the unknown parameters of the approximate nonlinear model described by the terminal nodes of the fuzzy decision tree. They present valuable and interpretable knowledge for the experts concerning the essence of the unknown phenomena.

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Author information

Authors and Affiliations

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

    Piotr Dziwiński & Łukasz Bartczuk

  2. Information Technology Institute, University of Social Sciences, 90-113, Łodz, Poland

    Krzysztof Przybyszewski

  3. Clark University, Worcester, MA, 01610, USA

    Krzysztof Przybyszewski

Authors
  1. Piotr Dziwiński
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  2. Łukasz Bartczuk
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  3. Krzysztof Przybyszewski
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Corresponding authors

Correspondence to Piotr Dziwiński , Łukasz Bartczuk or Krzysztof Przybyszewski .

Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Częstochowa, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Rafał Scherer

  3. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  4. University of Alberta, Edmonton, AB, Canada

    Witold Pedrycz

  5. AGH University of Science and Technology, Kraków, Poland

    Ryszard Tadeusiewicz

  6. University of Louisville, Louisville, KY, USA

    Jacek M. Zurada

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Dziwiński, P., Bartczuk, Ł., Przybyszewski, K. (2018). A Population Based Algorithm and Fuzzy Decision Trees for Nonlinear Modeling. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2018. Lecture Notes in Computer Science(), vol 10842. Springer, Cham. https://doi.org/10.1007/978-3-319-91262-2_46

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  • DOI: https://doi.org/10.1007/978-3-319-91262-2_46

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