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Design of Rule-Based Neurofuzzy Networks by Means of Genetic Fuzzy Set-Based Granulation

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Advances in Neural Networks – ISNN 2005 (ISNN 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3496))

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Abstract

In this paper, new architectures and design methodologies of Rule based Neurofuzzy Networks (RNFN) 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 RNFN is based on the fuzzy set based neurofuzzy networks (NFN) 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 dynamic search-based GAs optimizes the structure and parameters of the RNFN.

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Author information

Authors and Affiliations

  1. School of Electrical, Electronic and Information Engineering, Wonkwang University, South Korea

    Byoungjun Park

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

    Sungkwun Oh

Authors
  1. Byoungjun Park
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  2. Sungkwun Oh
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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. The Key Laboratory of Optoelectric Technology & Systems, Ministry of Education, China

    Xiaofeng Liao

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

    Zhang Yi

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© 2005 Springer-Verlag Berlin Heidelberg

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Park, B., Oh, S. (2005). Design of Rule-Based Neurofuzzy Networks by Means of Genetic Fuzzy Set-Based Granulation. In: Wang, J., Liao, X., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3496. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427391_67

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  • DOI: https://doi.org/10.1007/11427391_67

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25912-1

  • Online ISBN: 978-3-540-32065-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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