Design of evolutionally optimized rule-based fuzzy neural networks based on fuzzy relation and evolutionary optimization

Authors:
Byoung-Jun Park;Sung-Kwun Oh;Witold Pedrycz;Hyun-Ki Kim
Affiliations:
Department of Electrical Electronic and Information Engineering, Wonkwang University, Chon-Buk, South Korea;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 Engineering, The University of Suwon, Gyeonggi-do, South Korea
Venue:
ICCS'05 Proceedings of the 5th international conference on Computational Science - Volume Part III
Year:
2005

Citing 1
Cited 1

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Abstract

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