A classification technique based on radial basis function neural networks

Sarimveis, H; Doganis, P; Alexandridis, A

dc.contributor.author	Sarimveis, H	en
dc.contributor.author	Doganis, P	en
dc.contributor.author	Alexandridis, A	en
dc.date.accessioned	2014-03-01T01:23:22Z
dc.date.available	2014-03-01T01:23:22Z
dc.date.issued	2006	en
dc.identifier.issn	0965-9978	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/16931
dc.subject	Classification	en
dc.subject	Fuzzy means	en
dc.subject	Neural networks	en
dc.subject	Quality properties	en
dc.subject	Radial basis functions	en
dc.subject.classification	Computer Science, Interdisciplinary Applications	en
dc.subject.classification	Computer Science, Software Engineering	en
dc.subject.other	Computer architecture	en
dc.subject.other	Data acquisition	en
dc.subject.other	Fuzzy sets	en
dc.subject.other	Online systems	en
dc.subject.other	Product development	en
dc.subject.other	Sensors	en
dc.subject.other	Classification	en
dc.subject.other	Fuzzy means	en
dc.subject.other	Quality properties	en
dc.subject.other	Radial basis functions	en
dc.subject.other	Radial basis function networks	en
dc.title	A classification technique based on radial basis function neural networks	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.advengsoft.2005.07.005	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.advengsoft.2005.07.005	en
heal.language	English	en
heal.publicationDate	2006	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. (c) 2005 Elsevier Ltd. All rights reserved.	en
heal.publisher	ELSEVIER SCI LTD	en
heal.journalName	Advances in Engineering Software	en
dc.identifier.doi	10.1016/j.advengsoft.2005.07.005	en
dc.identifier.isi	ISI:000235772700002	en
dc.identifier.volume	37	en
dc.identifier.issue	4	en
dc.identifier.spage	218	en
dc.identifier.epage	221	en