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New Method for Design of Fuzzy Systems for Nonlinear Modelling Using Different Criteria of Interpretability

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Artificial Intelligence and Soft Computing (ICAISC 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8467))

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Abstract

In this paper a new method for designing neuro-fuzzy systems for nonlinear modelling is proposed. This method contains a complex weighted fitness function with interpretability criteria and new enhanced tuning process for selecting parameters and structure of the system based on a hybrid population-based algorithm (composed of evolutionary strategy, genetic algorithm and bees algorithm). To evaluate this method, we used a well-known dynamic nonlinear modelling problem.

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Authors and Affiliations

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

    Krystian Łapa & Krzysztof Cpałka

  2. School of Electrical & Electronic Engineering, Nanyang Technological University, Singapore

    Lipo Wang

Authors
  1. Krystian Łapa
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  2. Krzysztof Cpałka
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  3. Lipo Wang
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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, Mickiewicza 30, 30-059, Kraków, Poland

    Ryszard Tadeusiewicz

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

    Lotfi A. Zadeh

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

    Jacek M. Zurada

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Łapa, K., Cpałka, K., Wang, L. (2014). New Method for Design of Fuzzy Systems for Nonlinear Modelling Using Different Criteria of Interpretability. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2014. Lecture Notes in Computer Science(), vol 8467. Springer, Cham. https://doi.org/10.1007/978-3-319-07173-2_20

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