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

dc.contributor.author	Σαρίμβεης, Χαράλαμπος Κ.	el
dc.contributor.author	Αλεξανδρίδης, Αλέξανδρος Π.	el
dc.contributor.author	Μαζαράκης, Στέφανος	el
dc.contributor.author	Μπάφας, Γιώργος Β.	el
dc.date.accessioned	2015-06-04T14:24:41Z
dc.date.available	2015-06-04T14:24:41Z
dc.date.issued	2015-06-04
dc.identifier.uri	http://hdl.handle.net/11400/15069
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	Dynamic modeling
dc.subject	Genetic algorithms
dc.subject	Model structure optimization
dc.subject	Radial basis functions
dc.subject	RBF networks
dc.subject	Δυναμική μοντελοποίηση
dc.subject	Γενετικοί αλγόριθμοι
dc.subject	Μοντέλο βελτιστοποίησης της δομής
dc.subject	Συναρτήσεις ακτινικής βάσης
dc.title	A new algorithm for developing dynamic radial basis function neural network models based on genetic algorithms	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/sh92002377
heal.keywordURI	http://id.loc.gov/authorities/subjects/sh2002004691
heal.identifier.secondary	DOI: 10.1016/S0098-1354(03)00169-8
heal.language	en
heal.access	campus
heal.publicationDate	2004-01-15
heal.bibliographicCitation	SARIMVEIS, H.K., ALEXANDRIDIS, A.P., MAZARAKIS, S. & BAFAS, G.V. (2004). A new algorithm for developing dynamic radial basis function neural network models based on genetic algorithms. Computers and Chemical Engineering. [online] 28 (1-2). p. 209-217. Available from: http://www.elsevier.com/[Accessed 27/11/2003]	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.	en
heal.publisher	Elsevier	en
heal.journalName	Computers and Chemical Engineering	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	true