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

Wallace, M; Tsapatsoulis, N

dc.contributor.author	Wallace, M	en
dc.contributor.author	Tsapatsoulis, N	en
dc.date.accessioned	2014-03-01T02:43:10Z
dc.date.available	2014-03-01T02:43:10Z
dc.date.issued	2005	en
dc.identifier.issn	0302-9743	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/31270
dc.subject	Fuzzy Rules	en
dc.subject	Genetic Algorithm	en
dc.subject	rbf neural network	en
dc.subject	Resource Allocation	en
dc.subject	Unsupervised Clustering	en
dc.subject.classification	Computer Science, Theory & Methods	en
dc.subject.other	Artificial intelligence	en
dc.subject.other	Computer science	en
dc.subject.other	Data structures	en
dc.subject.other	Fuzzy control	en
dc.subject.other	Genetic algorithms	en
dc.subject.other	Numerical analysis	en
dc.subject.other	Optimization	en
dc.subject.other	Resource allocation	en
dc.subject.other	Clustering techniques	en
dc.subject.other	Fuzzy rule extraction	en
dc.subject.other	Input dimensions	en
dc.subject.other	Resource Allocating Networks	en
dc.subject.other	Radial basis function networks	en
dc.title	Combining GAs and RBF neural networks for fuzzy rule extraction from numerical data	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1007/11550907_82	en
heal.identifier.secondary	http://dx.doi.org/10.1007/11550907_82	en
heal.language	English	en
heal.publicationDate	2005	en
heal.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. © Springer-Verlag Berlin Heidelberg 2005.	en
heal.publisher	SPRINGER-VERLAG BERLIN	en
heal.journalName	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en
heal.bookName	LECTURE NOTES IN COMPUTER SCIENCE	en
dc.identifier.doi	10.1007/11550907_82	en
dc.identifier.isi	ISI:000232196000082	en
dc.identifier.volume	3697 LNCS	en
dc.identifier.spage	521	en
dc.identifier.epage	526	en