An evolutionary-based approach in RBF neural network training

dc.contributor.author	Αλεξανδρίδης, Αλέξανδρος Π.	el
dc.date.accessioned	2015-06-03T18:57:51Z
dc.date.issued	2015-06-03
dc.identifier.uri	http://hdl.handle.net/11400/15006
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	Evolutionary computation
dc.subject	Genetic algorithms
dc.subject	Non-symmetric Fuzzy Means
dc.subject	Radial basis functions
dc.subject	Εξελικτική Υπολογιστική
dc.subject	Γενετικοί Αλγόριθμοι
dc.subject	Ακτινική συνάρτηση βάσης
dc.title	An evolutionary-based approach in RBF neural network training	en
heal.type	conferenceItem
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/sh92002377
heal.keywordURI	http://id.loc.gov/authorities/subjects/sh2002004691
heal.identifier.secondary	DOI: 10.1109/EAIS.2012.6232817
heal.dateAvailable	10000-01-01
heal.language	en
heal.access	forever
heal.publicationDate	2012
heal.bibliographicCitation	Alexandridis, A.P. (2012) An evolutionary-based approach in RBF neural network training, In: Proceedings of the IEEE Conference on Evolving and Adaptive Intelligent Systems, EAIS 2012. Madrid, Spain. 17-18 May, 2012. [online]. p. 127-132, 6232817. Available from: http://ieeexplore.ieee.org/	en
heal.abstract	This paper presents a methodology for evolving populations of Radial Basis Function (RBF) networks, in order to optimize the accuracy of the corresponding model predictions. The method encodes possible non-symmetric fuzzy partitions of the input space as chromosomes and then uses the non-symmetric fuzzy means algorithm to deploy an RBF network for each partition. The chromosomes are evolved through the use of a specially designed Genetic Algorithm, thus resulting to improved RBF models. The proposed approach has been applied successfully to neural network training benchmark problems.	en
heal.publisher	IEEE	en
heal.fullTextAvailability	false
heal.conferenceName	IEEE Conference on Evolving and Adaptive Intelligent Systems, EAIS 2012	en
heal.conferenceItemType	poster