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	2014-03-01T02:42:24Z
dc.date.available	2014-03-01T02:42:24Z
dc.date.issued	2004	en
dc.identifier.issn	0098-1354	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/30993
dc.subject	Dynamic modeling	en
dc.subject	Genetic algorithms	en
dc.subject	Model structure optimization	en
dc.subject	Radial basis functions	en
dc.subject	RBF networks	en
dc.subject.classification	Computer Science, Interdisciplinary Applications	en
dc.subject.classification	Engineering, Chemical	en
dc.subject.other	Computer simulation	en
dc.subject.other	Data acquisition	en
dc.subject.other	Error analysis	en
dc.subject.other	Genetic algorithms	en
dc.subject.other	Optimization	en
dc.subject.other	Problem solving	en
dc.subject.other	Pulp	en
dc.subject.other	Paper industry	en
dc.subject.other	Neural networks	en
dc.subject.other	algorithm	en
dc.subject.other	neural network	en
dc.subject.other	algorithm	en
dc.subject.other	analytical error	en
dc.subject.other	computer model	en
dc.subject.other	conference paper	en
dc.subject.other	data base	en
dc.subject.other	genetic algorithm	en
dc.subject.other	information processing	en
dc.subject.other	nerve cell network	en
dc.subject.other	paper industry	en
dc.subject.other	prediction	en
dc.subject.other	process model	en
dc.subject.other	reactor	en
dc.title	A new algorithm for developing dynamic radial basis function neural network models based on genetic algorithms	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1016/S0098-1354(03)00169-8	en
heal.identifier.secondary	http://dx.doi.org/10.1016/S0098-1354(03)00169-8	en
heal.language	English	en
heal.publicationDate	2004	en
heal.abstract	A new method for extracting valuable process information from input-output data is presented in this paper. The proposed methodology produces dynamical radial basis function (RBF) neural network models based on a specially designed genetic algorithm (GA), which is used to auto-configure the structure of the networks and obtain the model parameters. The new RBF network training technique formulates a complete optimization problem, which includes the network structure into the set of free variables that are used to minimize the prediction error. This is a different approach compared with the local search methods employed by other structure selection mechanisms, which are often trapped to local minima. Another advantage of the proposed method is that only one run of the algorithm is required to obtain the optimal network structure, in contrast to the standard RBF training techniques, where the produced model is selected by trial and error. The effectiveness of the method is illustrated through the development of dynamical models for two sets of data: simulated data from a Continuous Stirred Tank Reactor (CSTR) and true data collected from a Kamyr digester, which is a rather complicated reactor used in the pulp and paper industry. (C) 2003 Elsevier Ltd. All rights reserved.	en
heal.publisher	PERGAMON-ELSEVIER SCIENCE LTD	en
heal.journalName	Computers and Chemical Engineering	en
dc.identifier.doi	10.1016/S0098-1354(03)00169-8	en
dc.identifier.isi	ISI:000187895900020	en
dc.identifier.volume	28	en
dc.identifier.issue	1-2	en
dc.identifier.spage	209	en
dc.identifier.epage	217	en