A classification technique based on radial basis function neural networks

dc.contributor.author	Σαρίμβεης, Χαράλαμπος Κ.	el
dc.contributor.author	Δογάνης, Φίλιππος	el
dc.contributor.author	Αλεξανδρίδης, Αλέξανδρος Π.	el
dc.date.accessioned	2015-06-04T12:45:14Z
dc.date.available	2015-06-04T12:45:14Z
dc.date.issued	2015-06-04
dc.identifier.uri	http://hdl.handle.net/11400/15055
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	Classification
dc.subject	Fuzzy means
dc.subject	Neural networks
dc.subject	Quality properties
dc.subject	Radial basis functions
dc.subject	Ταξινόμηση
dc.subject	Ασαφή μέσα
dc.subject	Νευρωνικά δίκτυα
dc.subject	Ποιοτικές ιδιότητες
dc.subject	Ακτινικές συναρτήσεις βάσεις
dc.title	A classification technique based on radial basis function neural networks	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: 10.1016/j.advengsoft.2005.07.005
heal.language	en
heal.access	campus
heal.publicationDate	2006-04
heal.bibliographicCitation	SARIMVEIS, H.K., DOGANIS, P. & ALEXANDRIDIS, A.P. (2006). A classification technique based on radial basis function neural networks. Advances in Engineering Software. [online] 37 (4). p. 218-221. Available from: http://www.elsevier.com/[Accessed 09/09/2005]	en
heal.abstract	In this paper, a new classification method is proposed based on the radial basis function (RBF) neural network architecture. The method is particularly useful for manufacturing processes, in cases where on-line sensors for classifying the product quality are not available. More specifically, the fuzzy means algorithm is employed on a set of training data, where the input data refer to variables that are measured on-line and the output data correspond to quality variables that are classified by human experts. The produced neural network model acts as an artificial sensor that is able to classify the product quality in real time. The proposed method is illustrated through an application to real data collected from a paper machine. The method produces successful results and outperforms a number of classifiers, which are based on the feedforward neural network (FNN) architecture.	en
heal.publisher	Elsevier	en
heal.journalName	Advances in Engineering Software	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	true