A new approach to genetically optimized hybrid fuzzy set-based polynomial neural networks with FSPNs and PNs

Authors:
Sung-Kwun Oh;Seok-Beom Roh;Witold Pedrycz
Affiliations:
Department of Electrical Engineering, The University of Suwon, Hwaseong-si, Gyeonggi-do, South Korea;Department of Electrical Electronic and Information Engineering, Wonkwang University, Chon-Buk, South Korea;Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada
Venue:
MDAI'05 Proceedings of the Second international conference on Modeling Decisions for Artificial Intelligence
Year:
2005

Citing 4
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Abstract

We investigate a new fuzzy-neural networks-Hybrid Fuzzy set based polynomial Neural Networks (HFSPNN). These networks consist of genetically optimized multi-layer with two kinds of heterogeneous neurons that are fuzzy set based polynomial neurons (FSPNs) and polynomial neurons (PNs). We have developed a comprehensive design methodology to determine the optimal structure of networks dynamically. The augmented genetically optimized HFSPNN (namely gHFSPNN) results in a structurally optimized structure and comes with a higher level of flexibility in comparison to the one we encounter in the conventional HFPNN. The GA-based design procedure being applied at each layer of gHFSPNN leads to the selection leads to the selection of preferred nodes (FSPNs or PNs) available within the HFSPNN. In the sequel, the structural optimization is realized via GAs, whereas the ensuing detailed parametric optimization is carried out in the setting of a standard least square method-based learning. The performance of the gHFSPNN is quantified through experimentation where we use a number of modeling benchmarks-synthetic and experimental data already experimented with in fuzzy or neurofuzzy modeling.