dc.contributor.author | Σαρίμβεης, Χαράλαμπος Κ. | el |
dc.contributor.author | Αλεξανδρίδης, Αλέξανδρος Π. | el |
dc.contributor.author | Μπάφας, Γιώργος Β. | el |
dc.date.accessioned | 2015-06-04T14:56:47Z | |
dc.date.available | 2015-06-04T14:56:47Z | |
dc.date.issued | 2015-06-04 | |
dc.identifier.uri | http://hdl.handle.net/11400/15073 | |
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 | Model selection | |
dc.subject | Radial basis function networks | |
dc.subject | Training algorithms | |
dc.subject | Επιλογή μοντέλου | |
dc.subject | Δίκτυα συνάρτησης ακτινικής βάσης | |
dc.subject | Αλγόριθμοι κατάρτισης | |
dc.title | A fast training algorithm for RBF networks based on subtractive clustering | 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.identifier.secondary | DOI: 10.1016/S0925-2312(03)00342-4 | |
heal.language | en | |
heal.access | campus | |
heal.publicationDate | 2003-04 | |
heal.bibliographicCitation | SARIMVEIS, H.K., ALEXANDRIDIS, A.P. & BAFAS, G.V. (2003). A fast training algorithm for RBF networks based on subtractive clustering. Neurocomputing. [online] 51. p. 501-505. Available from: http://www.elsevier.com/[Accessed 12/02/2003] | 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. | en |
heal.publisher | Elsevier | en |
heal.journalName | Neurocomputing | en |
heal.journalType | peer-reviewed | |
heal.fullTextAvailability | true |
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