Variable selection in nonlinear modeling based on RBF networks and evolutionary computation

dc.contributor.author	Πατρινός, Παναγιώτης Κ.	el
dc.contributor.author	Αλεξανδρίδης, Αλέξανδρος Π.	el
dc.contributor.author	Νίνος, Κωνσταντίνος Δ.	el
dc.contributor.author	Σαρίμβεης, Χαράλαμπος Κ.	el
dc.date.accessioned	2015-06-03T20:46:01Z
dc.date.issued	2015-06-03
dc.identifier.uri	http://hdl.handle.net/11400/15013
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.worldscientific.com/	en
dc.subject	Evolutionary computation
dc.subject	gas furnace data
dc.subject	Mackey glass data
dc.subject	neural networks
dc.subject	quantitative structure activity relationship (QSAR)
dc.subject	Radial basis functions
dc.subject	Variable selection
dc.subject	εξελικτικοί αλγόριθμοι
dc.subject	νευρωνικά δίκτυα
dc.subject	ακτινικές συναρτήσεις βάσης
dc.subject	επιλογή μεταβλητής
dc.title	Variable selection in nonlinear modeling based on RBF networks and evolutionary computation	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/sh95003989
heal.keywordURI	http://id.loc.gov/authorities/subjects/sh2002004691
heal.identifier.secondary	DOI: 10.1142/S0129065710002474
heal.dateAvailable	10000-01-01
heal.language	en
heal.access	forever
heal.publicationDate	2010-10
heal.bibliographicCitation	PATRINOS, P.K., ALEXANDRIDIS, A.P., NINOS, K.D. & SARIMVEIS, H.K. (2010). Variable selection in nonlinear modeling based on RBF networks and evolutionary computation. International Journal of Neural Systems. [online] 20 (5). p. 365-379. Available from: http://www.worldscientific.com/	en
heal.abstract	In this paper a novel variable selection method based on Radial Basis Function (RBF) neural networks and genetic algorithms is presented. The fuzzy means algorithm is utilized as the training method for the RBF networks, due to its inherent speed, the deterministic approach of selecting the hidden node centers and the fact that it involves only a single tuning parameter. The trade-off between the accuracy and parsimony of the produced model is handled by using Final Prediction Error criterion, based on the RBF training and validation errors, as a fitness function of the proposed genetic algorithm. The tuning parameter required by the fuzzy means algorithm is treated as a free variable by the genetic algorithm. The proposed method was tested in benchmark data sets stemming from the scientific communities of time-series prediction and medicinal chemistry and produced promising results.	en
heal.publisher	World Scientific Publishing	en
heal.journalName	International Journal of Neural Systems	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	false