An advanced design methodology of fuzzy set-based polynomial neural networks with the aid of symbolic gene type genetic algorithms and information granulation

Authors:
Seok-Beom Roh;Hyung-Soo Hwang;Tae-Chon Ahn
Affiliations:
Department of Electrical Electronic and Information Engineering, Wonkwang University, Shinyong-Dong, Iksan, Chon-Buk, South Korea;Department of Electrical Electronic and Information Engineering, Wonkwang University, Shinyong-Dong, Iksan, Chon-Buk, South Korea;Department of Electrical Electronic and Information Engineering, Wonkwang University, Shinyong-Dong, Iksan, Chon-Buk, South Korea
Venue:
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
Year:
2006

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Abstract

In this paper, we propose a new design methodology that adopts Information Granulation to the structure of fuzzy-neural networks called Fuzzy Set-based Polynomial Neural Networks (FSPNN). We find the optimal structure of the proposed model with the aid of symbolic genetic algorithms which has symbolic gene type chromosomes. We are able to find information related to real system with Information Granulation through numerical data. Information Granules obtained from Information Granulation help us understand real system without the field expert. In Information Granulation, we use conventional Hard C-Means Clustering algorithm and proposed procedure that handle the apex of clusters using 'Union' and 'Intersection' operation. We use genetic algorithm to find optimal structure of the proposed networks. The proposed networks are based on GMDH algorithm that makes whole networks dynamically. In other words, FSPNN is built dynamically with symbolic genetic algorithms. Symbolic gene type has better characteristic than binary coding GAs from the size of solution space's point of view. . Symbolic genetic algorithms are capable of reducing the solution space more than conventional genetic algorithms with binary genetype chromosomes. The performance of genetically optimized FSPNN (gFSPNN) with aid of symbolic genetic algorithms is quantified through experimentation where we use a number of modeling benchmarks data which are already experimented with in fuzzy or neurofuzzy modeling.