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A New Method for Designing and Complexity Reduction of Neuro-fuzzy Systems for Nonlinear Modelling

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Artificial Intelligence and Soft Computing (ICAISC 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7894))

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Abstract

In this paper we propose a new method for evolutionary selection of parameters and structure of neuro-fuzzy system for nonlinear modelling. This method allows maintain the correct proportions between accuracy, complexity and interpretability of the system. Our algorithm has been tested using well-known benchmarks.

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Authors and Affiliations

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

    Krystian Łapa, Marcin Zalasiński & Krzysztof Cpałka

Authors
  1. Krystian Łapa
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  2. Marcin Zalasiński
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  3. Krzysztof Cpałka
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Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Armii Krajowej 36, 42-200, Częstochowa, Poland

    Leszek Rutkowski , Marcin Korytkowski  & Rafał Scherer ,  & 

  2. AGH University of Science and Technology, Michiewicza 30, 30-059, Kraków, Poland

    Ryszard Tadeusiewicz

  3. Department of Electrical Engineering and Computer Sciences, University of California, 94720-1776, Berkeley, CA, USA

    Lotfi A. Zadeh

  4. Electrical and Computer Engineering, University of Louisville, 405 Lutz Hall, 40292, Louisville, KY, USA

    Jacek M. Zurada

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Łapa, K., Zalasiński, M., Cpałka, K. (2013). A New Method for Designing and Complexity Reduction of Neuro-fuzzy Systems for Nonlinear Modelling. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2013. Lecture Notes in Computer Science(), vol 7894. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38658-9_30

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  • DOI: https://doi.org/10.1007/978-3-642-38658-9_30

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