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A Method for Nonlinear Fuzzy Modelling Using Population Based Algorithm with Flexibly Selectable Operators

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Artificial Intelligence and Soft Computing (ICAISC 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10245))

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Abstract

In this paper a new method based on a population-based algorithm with flexible selectable operators for nonlinear modeling is proposed. This method enables usage of any types of exploration and exploitation operators, typical for population-based algorithms. Moreover, in proposed approach each solution from population encodes activity and parameters of these operators. Due to this, they can be selected dynamically in the evolution process. Such approach eliminates the need for determining detailed mechanism of the population-based algorithm. For the simulations typical nonlinear modeling benchmarks were used.

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The project was financed by the National Science Centre (Poland) on the basis of the decision number DEC-2012/05/B/ST7/02138.

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Authors and Affiliations

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

    Krystian Łapa & Krzysztof Cpałka

  2. Nanyang Technological University, Central Area, Singapore

    Lipo Wang

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  1. Krystian Łapa
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  2. Krzysztof Cpałka
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  3. Lipo Wang
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Correspondence to Krystian Łapa .

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Editors and Affiliations

  1. Częstochowa University of Technology, Częstochowa, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  3. Częstochowa University of Technology, Częstochowa, Poland

    Rafał Scherer

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

    Ryszard Tadeusiewicz

  5. University of California, Berkeley, California, USA

    Lotfi A. Zadeh

  6. University of Louisville, Louisville, Kentucky, USA

    Jacek M. Zurada

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Łapa, K., Cpałka, K., Wang, L. (2017). A Method for Nonlinear Fuzzy Modelling Using Population Based Algorithm with Flexibly Selectable Operators. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2017. Lecture Notes in Computer Science(), vol 10245. Springer, Cham. https://doi.org/10.1007/978-3-319-59063-9_24

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