Radial basis function network training using a nonsymmetric partition of the input space and particle swarm optimization

dc.contributor.author	Αλεξανδρίδης, Αλέξανδρος Π.	el
dc.contributor.author	Χονδροδήμα, Ευαγγελία	el
dc.contributor.author	Σαρίμβεης, Χαράλαμπος Κ.	el
dc.date.accessioned	2015-06-03T18:51:42Z
dc.date.issued	2015-06-03
dc.identifier.uri	http://hdl.handle.net/11400/15005
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://ieeexplore.ieee.org/	en
dc.subject	Fuzzy means algorithm
dc.subject	Fuzzy partition
dc.subject	Nonsymmetric partition
dc.subject	Particle swarm optimization
dc.subject	Radial basis functions
dc.subject	Αλγόριθμος ασαφών μέσων
dc.subject	Βελτιστοποίηση σμήνους σωματιδίων
dc.subject	Ακτινική συνάρτηση βάσης
dc.title	Radial basis function network training using a nonsymmetric partition of the input space and particle swarm optimization	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: 10.1109/TNNLS.2012.2227794
heal.dateAvailable	10000-01-01
heal.language	en
heal.access	forever
heal.publicationDate	2013
heal.bibliographicCitation	ALEXANDRIDIS, A.P., CHONDRODIMA, E. & SARIMVEIS, H.K. (2013). Radial basis function network training using a nonsymmetric partition of the input space and particle swarm optimization. IEEE Transactions on Neural Networks and Learning Systems. [online] 24 (2). p. 219-230, 6395832. Available from: http://ieeexplore.ieee.org/	en
heal.abstract	This paper presents a novel algorithm for training radial basis function (RBF) networks, in order to produce models with increased accuracy and parsimony. The proposed methodology is based on a nonsymmetric variant of the fuzzy means (FM) algorithm, which has the ability to determine the number and locations of the hidden-node RBF centers, whereas the synaptic weights are calculated using linear regression. Taking advantage of the short computational times required by the FM algorithm, we wrap a particle swarm optimization (PSO) based engine around it, designed to optimize the fuzzy partition. The result is an integrated framework for fully determining all the parameters of an RBF network. The proposed approach is evaluated through its application on 12 real-world and synthetic benchmark datasets and is also compared with other neural network training techniques. The results show that the RBF network models produced by the PSO-based nonsymmetric FM algorithm outperform the models produced by the other techniques, exhibiting higher prediction accuracies in shorter computational times, accompanied by simpler network structures.	en
heal.publisher	IEEE	en
heal.journalName	IEEE Transactions on Neural Networks and Learning Systems	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	false