A new algorithm for developing dynamic radial basis function neural network models based on genetic algorithms

Sarimveis, H; Alexandridis, A; Mazarakis, S; Bafas, G

dc.contributor.author	Sarimveis, H	en
dc.contributor.author	Alexandridis, A	en
dc.contributor.author	Mazarakis, S	en
dc.contributor.author	Bafas, G	en
dc.date.accessioned	2014-03-01T01:17:22Z
dc.date.available	2014-03-01T01:17:22Z
dc.date.issued	2002	en
dc.identifier.issn	15707946	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/14493
dc.subject	Chemical Reactors	en
dc.subject	Dynamic Model	en
dc.subject	Genetic Algorithm	en
dc.subject	Pulp and Paper Industry	en
dc.subject	Radial Basis Function	en
dc.subject	rbf neural network	en
dc.subject	Radial Basis Function Neural Network	en
dc.title	A new algorithm for developing dynamic radial basis function neural network models based on genetic algorithms	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/S1570-7946(02)80186-9	en
heal.identifier.secondary	http://dx.doi.org/10.1016/S1570-7946(02)80186-9	en
heal.publicationDate	2002	en
heal.abstract	A new algorithm for extracting valuable information from industrial data is presented in this paper. The proposed methodology produces dynamic Radial Basis Function (RBF) neural network models and uses Genetic Algorithms (GAs) to auto-configure the structure of the networks. The effectiveness of the method is illustrated through the development of a dynamical model for a chemical reactor, used in pulp and paper industry. © 2002 Elsevier B.V. All rights reserved.	en
heal.journalName	Computer Aided Chemical Engineering	en
dc.identifier.doi	10.1016/S1570-7946(02)80186-9	en
dc.identifier.volume	10	en
dc.identifier.issue	C	en
dc.identifier.spage	949	en
dc.identifier.epage	954	en