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Design of Fuzzy Neural Networks Based on Genetic Fuzzy Granulation and Regression Polynomial Fuzzy Inference

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Advances in Neural Networks - ISNN 2006 (ISNN 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3971))

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Abstract

In this paper, new architectures and comprehensive design methodologies of Genetic Algorithms (GAs) based Fuzzy Relation-based Fuzzy Neural Networks (FRFNN) are introduced and the dynamic search-based GAs is introduced to lead to rapidly optimal convergence over a limited region or a boundary condition. The proposed FRFNN is based on the Fuzzy Neural Networks (FNN) with the extended structure of fuzzy rules being formed within the networks. In the consequence part of the fuzzy rules, three different forms of the regression polynomials such as constant, linear and modified quadratic are taken into consideration. The structure and parameters of the FRFNN are optimized by the dynamic search-based GAs. The proposed model is contrasted with the performance of conventional FNN models in the literature.

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Author information

Authors and Affiliations

  1. Department of Electrical Engineering, The University of Suwon, San 2-2 Wau-ri, Bongdam-eup, Hwaseong-si, Gyeonggi-do, 445-743, South Korea

    Sung-Kwun Oh

  2. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, T6G 2G6, Canada

    Byoung-Jun Park & Witold Pedrycz

  3. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Witold Pedrycz

Authors
  1. Sung-Kwun Oh
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  2. Byoung-Jun Park
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  3. Witold Pedrycz
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Editor information

Editors and Affiliations

  1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  2. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

  3. Department of Electrical Engineering, University of Louisville, 40292, Louisville, KY, U.S.A

    Jacek M. Zurada

  4. Laboratory for Computational Biology, Shanghai Center for Systems Biomedicine, 800 Dong Chuan Rd., 200240, Shanghai, China

    Bao-Liang Lu

  5. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

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© 2006 Springer-Verlag Berlin Heidelberg

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Oh, SK., Park, BJ., Pedrycz, W. (2006). Design of Fuzzy Neural Networks Based on Genetic Fuzzy Granulation and Regression Polynomial Fuzzy Inference. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3971. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11759966_115

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  • DOI: https://doi.org/10.1007/11759966_115

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34439-1

  • Online ISBN: 978-3-540-34440-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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