A Radial Basis Function network training algorithm using a non-symmetric partition of the input space - Application to a Model Predictive Control configuration

Alexandridis, A; Sarimveis, H; Ninos, K

dc.contributor.author	Alexandridis, A	en
dc.contributor.author	Sarimveis, H	en
dc.contributor.author	Ninos, K	en
dc.date.accessioned	2014-03-01T01:35:00Z
dc.date.available	2014-03-01T01:35:00Z
dc.date.issued	2011	en
dc.identifier.issn	0965-9978	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/20943
dc.subject	Continuous stirred tank reactor	en
dc.subject	Fuzzy means	en
dc.subject	Fuzzy partition	en
dc.subject	Mackey-Glass	en
dc.subject	Model predictive control	en
dc.subject	Neural networks	en
dc.subject	Non-symmetric partition	en
dc.subject	Radial basis function	en
dc.subject.classification	Computer Science, Interdisciplinary Applications	en
dc.subject.classification	Computer Science, Software Engineering	en
dc.subject.other	Continuous stirred tank reactor	en
dc.subject.other	Fuzzy means	en
dc.subject.other	Fuzzy partition	en
dc.subject.other	Mackey-Glass	en
dc.subject.other	Non-symmetric partition	en
dc.subject.other	Radial basis functions	en
dc.subject.other	Learning algorithms	en
dc.subject.other	Neural networks	en
dc.subject.other	Predictive control systems	en
dc.subject.other	Radial basis function networks	en
dc.subject.other	Model predictive control	en
dc.title	A Radial Basis Function network training algorithm using a non-symmetric partition of the input space - Application to a Model Predictive Control configuration	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.advengsoft.2011.05.026	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.advengsoft.2011.05.026	en
heal.language	English	en
heal.publicationDate	2011	en
heal.abstract	This work presents the non-symmetric fuzzy means algorithm which is a new methodology for training Radial Basis Function neural network models. The method is based on a non-symmetric fuzzy partition of the space of input variables which results to networks with smaller structures and better approximation capabilities compared to other state-of-the-art training procedures. The lower modeling error and the smaller size of the produced models become particularly important when they are used in online applications. This is demonstrated by integrating the model produced by the proposed algorithm in a Model Predictive Control configuration, resulting in better control performance and shorter computational times. (C) 2011 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.2011.05.026	en
dc.identifier.isi	ISI:000293872100012	en
dc.identifier.volume	42	en
dc.identifier.issue	10	en
dc.identifier.spage	830	en
dc.identifier.epage	837	en