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

dc.contributor.author	Αλεξανδρίδης, Αλέξανδρος Π.	el
dc.contributor.author	Σαρίμβεης, Χαράλαμπος Κ.	el
dc.contributor.author	Μπάφας, Γιώργος Β.	el
dc.date.accessioned	2015-06-04T14:50:31Z
dc.date.available	2015-06-04T14:50:31Z
dc.date.issued	2015-06-04
dc.identifier.uri	http://hdl.handle.net/11400/15072
dc.rights	Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/us/	*
dc.source	http://www.elsevier.com/	en
dc.subject	Adaptive modeling
dc.subject	Dynamic modeling
dc.subject	Dynamic systems
dc.subject	Fuzzy clustering
dc.subject	Radial basis functions
dc.subject	Structure adaptation
dc.subject	Time varying systems
dc.subject	Training methods
dc.subject	μέθοδοι κατάρτισης
dc.subject	Προσαρμοστικά μοντέλα
dc.subject	Δυναμική μοντελοποίηση
dc.subject	Δυναμικά συστήματα
dc.subject	Ασαφής ομαδοποίηση
dc.subject	Συνάρτηση ακτινικής βάσης
dc.subject	Προσαρμογή δομής
dc.title	A new algorithm for online structure and parameter adaptation of RBF networks	en
heal.type	journalArticle
heal.classification	Technology
heal.classification	Electrical engineering
heal.classification	Τεχνολογία
heal.classification	Ηλεκτρολογία Μηχανολογία
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh85133147
heal.classificationURI	http://zbw.eu/stw/descriptor/18426-4
heal.classificationURI	N/A-Τεχνολογία
heal.classificationURI	N/A-Ηλεκτρολογία Μηχανολογία
heal.keywordURI	http://id.loc.gov/authorities/subjects/sh2002004691
heal.identifier.secondary	DOI: 10.1016/S0893-6080(03)00052-2
heal.language	en
heal.access	campus
heal.publicationDate	2003-09
heal.bibliographicCitation	ALEXANDRIDIS, A.P., SARIMVEIS, H.K. & BAFAS, G.V. (2003). A new algorithm for online structure and parameter adaptation of RBF networks. Neural Networks. [online] 16 (7). p. 1003-1017. Available from: http://www.elsevier.com/[Accessed 01/04/2003]	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.	en
heal.publisher	Elsevier	en
heal.journalName	Neural Networks	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	true