Genetically optimized hybrid fuzzy neural networks with the aid of TSK fuzzy inference rules and polynomial neural networks

Authors:
Sung-Kwun Oh;Witold Pedrycz;Hyun-Ki Kim;Yong-Kab Kim
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 Engineering, The University of Suwon, Gyeonggi-do, South Korea;Department of Electrical Electronic and Information Engineering, Wonkwang University, Chon-Buk, South Korea
Venue:
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Year:
2005

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Abstract

We introduce an advanced architecture of genetically optimized Hybrid Fuzzy Neural Networks (gHFNN) and develop a comprehensive design methodology supporting their construction. The gHFNN architecture results from a synergistic usage of the hybrid system generated by combining Fuzzy Neural Networks (FNN) with Polynomial Neural Networks (PNN). We distinguish between two types of the linear fuzzy inference rule-based FNN structures showing how this taxonomy depends upon the type of a fuzzy partition of input variables. As to the consequence part of the gHFNN, the development of the PNN dwells on two general optimization mechanisms: the structural optimization is realized via GAs whereas in case of the parametric optimization we proceed with a standard least square method-based learning.