Genetically optimized hybrid fuzzy neural networks based on TSK fuzzy rules and polynomial neurons

Authors:
Sungkwun Oh;Byoungjun Park;Hyunki Kim
Affiliations:
Department of Electrical Engineering, The University of Suwon, Hwaseong-si, Gyeonggi-do, South Korea;Department of Electrical, Electronic and Information Engineering, Wonkwang University, Korea;Department of Electrical Engineering, The University of Suwon, Hwaseong-si, Gyeonggi-do, South Korea
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
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 (TSK fuzzy 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.