A new algorithm for online structure and parameter adaptation of RBF networks

Alexandridis, A; Sarimveis, H; Bafas, G

dc.contributor.author	Alexandridis, A	en
dc.contributor.author	Sarimveis, H	en
dc.contributor.author	Bafas, G	en
dc.date.accessioned	2014-03-01T01:18:32Z
dc.date.available	2014-03-01T01:18:32Z
dc.date.issued	2003	en
dc.identifier.issn	0893-6080	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/15075
dc.subject	Adaptive modeling	en
dc.subject	Dynamic modeling	en
dc.subject	Dynamic systems	en
dc.subject	Fuzzy clustering	en
dc.subject	Radial basis functions	en
dc.subject	Structure adaptation	en
dc.subject	Time varying systems	en
dc.subject	Training methods	en
dc.subject.classification	Computer Science, Artificial Intelligence	en
dc.subject.other	Algorithms	en
dc.subject.other	Extrapolation	en
dc.subject.other	Fuzzy sets	en
dc.subject.other	Problem solving	en
dc.subject.other	Radial basis function networks	en
dc.subject.other	Fuzzy subspaces	en
dc.subject.other	Neural networks	en
dc.subject.other	adaptation	en
dc.subject.other	algorithm	en
dc.subject.other	article	en
dc.subject.other	artificial neural network	en
dc.subject.other	dynamics	en
dc.subject.other	online system	en
dc.subject.other	priority journal	en
dc.subject.other	Adaptation, Biological	en
dc.subject.other	Algorithms	en
dc.subject.other	Neural Networks (Computer)	en
dc.title	A new algorithm for online structure and parameter adaptation of RBF networks	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/S0893-6080(03)00052-2	en
heal.identifier.secondary	http://dx.doi.org/10.1016/S0893-6080(03)00052-2	en
heal.language	English	en
heal.publicationDate	2003	en
heal.abstract	This paper deals with the problem of online adaptation of radial basis function (RBF) neural networks. A new adaptive training method is presented. which is able to modify both the structure of the network (the number of nodes in the hidden layer) and the output weights, as the algorithm proceeds. These adaptation capabilities make the algorithm suitable for modeling dynamical time varying systems, where not only the dynamics but also the operating region changes with time. Therefore, the important issue of extrapolation is faced successfully, but at the same time the algorithm takes care of the size of the network, by deleting the hidden node centers that remain inactive for a long time. The selection of the network centers is based on a fuzzy partition of the input space, which defines a number of fuzzy subspaces. The algorithm considers the centers of the fuzzy subspaces as candidates for becoming hidden node centers and makes the selections, so that at least one center is close enough to each input example. The proposed technique is illustrated through the application to time varying dynamical systems and is compared to other adaptive training methods. (C) 2003 Elsevier Science Ltd. All rights reserved.	en
heal.publisher	PERGAMON-ELSEVIER SCIENCE LTD	en
heal.journalName	Neural Networks	en
dc.identifier.doi	10.1016/S0893-6080(03)00052-2	en
dc.identifier.isi	ISI:000185078700006	en
dc.identifier.volume	16	en
dc.identifier.issue	7	en
dc.identifier.spage	1003	en
dc.identifier.epage	1017	en