A fast and efficient algorithm for training radial basis function neural networks based on a fuzzy partition of the input space

dc.contributor.author	Σαρίμβεης, Χαράλαμπος Κ.	el
dc.contributor.author	Αλεξανδρίδης, Αλέξανδρος Π.	el
dc.contributor.author	Τσεκούρας, Γεώργιος Ε.	el
dc.contributor.author	Μπάφας, Γιώργος Β.	el
dc.date.accessioned	2015-06-04T16:45:30Z
dc.date.issued	2015-06-04
dc.identifier.uri	http://hdl.handle.net/11400/15096
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://pubs.acs.org/	en
dc.subject	Computer simulation
dc.subject	Neural networks
dc.subject	Chemical engineering
dc.subject	υπολογιστική προσομοίωση
dc.subject	νευρωνικά δίκτυα
dc.subject	Χημική μηχανική
dc.title	A fast and efficient algorithm for training radial basis function neural networks based on a fuzzy partition of the input space	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://zbw.eu/stw/descriptor/19808-6
heal.keywordURI	http://id.loc.gov/authorities/subjects/sh85022900
heal.identifier.secondary	DOI: 10.1021/ie010263h
heal.dateAvailable	10000-01-01
heal.language	en
heal.access	forever
heal.publicationDate	2002-02-20
heal.bibliographicCitation	SARIMVEIS, H.K., ALEXANDRIDIS, A.P., TSEKOURAS, G.E. & BAFAS, G.V. (2002). A fast and efficient algorithm for training radial basis function neural networks based on a fuzzy partition of the input space. Industrial and Engineering Chemistry Research. [online] 41 (4). p. 751-759. Available from: http://pubs.acs.org/	en
heal.abstract	The popular radial basis function (RBF) neural network architecture and a new fast and efficient method for training such a network are used to model nonlinear dynamical multi-input multi-output (MIMO) discrete-time systems. The proposed training methodology is based on a fuzzy partition of the input space and combines self-organized and supervised learning. The algorithm is illustrated through the development of neural network models using simulated and experimental data. Results show that the methodology is much faster and produces more accurate models compared to the standard techniques used to train RBF networks. Another important advantage is that, for a given fuzzy partition of the input space, the proposed method is able to determine the proper network structure, without using a trial and error procedure.	en
heal.publisher	American Chemical Society	en
heal.journalName	Industrial and Engineering Chemistry Research	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	false