Evolutionally optimized fuzzy neural networks based on evolutionary fuzzy granulation

  • Authors:

  • Sung-Kwun Oh;Byoung-Jun Park;Witold Pedrycz;Hyun-Ki Kim

  • Affiliations:

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

  • Venue:
  • ICCSA'05 Proceedings of the 2005 international conference on Computational Science and Its Applications - Volume Part IV
  • Year:
  • 2005

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Abstract

In this paper, new architectures and comprehensive design methodologies of Genetic Algorithms (GAs) based Evolutionally optimized Fuzzy Neural Networks (EoFNN) 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 EoFNN 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 EoFNN are optimized by the dynamic search-based GAs.