Combining GAs and RBF neural networks for fuzzy rule extraction from numerical data

Authors:
Manolis Wallace;Nicolas Tsapatsoulis
Affiliations:
University of Indianapolis and School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece;School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece and Dept. of Computer Science, University of Cyprus, Nicosia, Cyprus
Venue:
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
Year:
2005

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Abstract

The idea of using RBF neural networks for fuzzy rule extraction from numerical data is not new. The structure of this kind of architectures, which supports clustering of data samples, is favorable for considering clusters as ifthen rules. However, in order for real if-then rules to be derived, proper antecedent parts for each cluster need to be constructed by selecting the appropriate subspace of input space that best matches each cluster's properties. In this paper we address the problem of antecedent part construction by (a) initializing the hidden layer of an RBF-Resource Allocating Network using an unsupervised clustering technique whose metric is based on input dimensions that best relate the data samples in a cluster, and (b) by pruning input connections to hidden nodes in a per node basis, using an innovative Genetic Algorithm optimization scheme.