Design of fuzzy neural networks based on genetic fuzzy granulation and regression polynomial fuzzy inference

  • Authors:

  • Sung-Kwun Oh;Byoung-Jun Park;Witold Pedrycz

  • Affiliations:

  • Department of Electrical Engineering, The University of Suwon, Gyeonggi-do, South Korea;Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada;Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada

  • Venue:
  • ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
  • Year:
  • 2006

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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.