Please use this identifier to cite or link to this item: http://ktisis.cut.ac.cy/handle/10488/163
Title: Combining GAs and RBF Neural Networks for Fuzzy Rule Extraction from Numerical Data
Authors: Wallace, Manolis 
Tsapatsoulis, Nicolas 
Keywords: Neural networks (Computer science)--Congresses
Artificial intelligence--Congresses
Issue Date: 2005
Publisher: Springer Berlin / Heidelberg
Source: Artificial Neural Networks: Formal Models and Their Applications - ICANN 2005, pp.521-526
Series/Report no.: Lecture Notes in Computer Science;
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 if-then 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.
Description: International Conference on Artificial Neural Networks (European Neural Network Society),(15th,2005,Warsaw,PolandV)
URI: http://ktisis.cut.ac.cy/handle/10488/163
ISBN: 9783540287551
DOI: 10.1007/11550907_82
Rights: © Springer
Appears in Collections:Κεφάλαια βιβλίων/Book chapters

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