A fast training algorithm for RBF networks based on subtractive clustering

Sarimveis, H; Alexandridis, A; Bafas, G

dc.contributor.author	Sarimveis, H	en
dc.contributor.author	Alexandridis, A	en
dc.contributor.author	Bafas, G	en
dc.date.accessioned	2014-03-01T01:18:31Z
dc.date.available	2014-03-01T01:18:31Z
dc.date.issued	2003	en
dc.identifier.issn	0925-2312	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/15060
dc.subject	Model selection	en
dc.subject	Radial basis function networks	en
dc.subject	Training algorithms	en
dc.subject.classification	Computer Science, Artificial Intelligence	en
dc.subject.other	Learning algorithms	en
dc.subject.other	Nonlinear systems	en
dc.subject.other	Personnel training	en
dc.subject.other	Subtractive clustering	en
dc.subject.other	Radial basis function networks	en
dc.subject.other	accuracy	en
dc.subject.other	algorithm	en
dc.subject.other	analytic method	en
dc.subject.other	article	en
dc.subject.other	artificial neural network	en
dc.subject.other	cluster analysis	en
dc.subject.other	learning	en
dc.subject.other	mathematical computing	en
dc.subject.other	nonlinear system	en
dc.subject.other	prediction	en
dc.subject.other	priority journal	en
dc.subject.other	statistical model	en
dc.subject.other	time	en
dc.subject.other	training	en
dc.title	A fast training algorithm for RBF networks based on subtractive clustering	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/S0925-2312(03)00342-4	en
heal.identifier.secondary	http://dx.doi.org/10.1016/S0925-2312(03)00342-4	en
heal.language	English	en
heal.publicationDate	2003	en
heal.abstract	A new algorithm for training radial basis function neural networks is presented in this paper. The algorithm, which is based on the subtractive clustering technique, has a number of advantages compared to the traditional learning algorithms, including faster training times and more accurate predictions. Due to these advantages the method proves suitable for developing models for complex nonlinear systems. (C) 2003 Elsevier Science B.V. All rights reserved.	en
heal.publisher	ELSEVIER SCIENCE BV	en
heal.journalName	Neurocomputing	en
dc.identifier.doi	10.1016/S0925-2312(03)00342-4	en
dc.identifier.isi	ISI:000181912600034	en
dc.identifier.volume	51	en
dc.identifier.spage	501	en
dc.identifier.epage	505	en