Evolutionally optimized fuzzy neural networks based on fuzzy relation rules and evolutionary data granulation

  • Authors:

  • Sung-Kwun Oh;Hyun-Ki Kim;Seong-Whan Jang;Yong-Kab Kim

  • Affiliations:

  • Department of Electrical Engineering, The University of Suwon, Gyeonggi-do, South Korea;Department of Electrical Engineering, The University of Suwon, Gyeonggi-do, South Korea;Department of Electrical Electronic and Information Engineering, Wonkwang University, Chon-Buk, South Korea;Department of Electrical Electronic and Information Engineering, Wonkwang University, Chon-Buk, South Korea

  • Venue:
  • AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
  • Year:
  • 2005

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Abstract

In this paper, we introduce new architectures and comprehensive design methodologies of Evolutionally optimized Fuzzy Neural Networks (EoFNN). The proposed dynamic search-based GAs leads 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 takes into consideration. The structure and parameters of the EoFNN are optimized by the dynamic search-based GAs.