Modelling of nonlinear process dynamics using Kohonen's neural networks, fuzzy systems and Chebyshev series

dc.contributor.author	Αλεξανδρίδης, Αλέξανδρος Π.	el
dc.contributor.author	Σιέττος, Κωνσταντίνος Ι.	el
dc.contributor.author	Σαρίμβεης, Χαράλαμπος Κ.	el
dc.contributor.author	Μπουντουβής, Ανδρέας Γ.	el
dc.contributor.author	Μπάφας, Γιώργος Β.	el
dc.date.accessioned	2015-06-04T16:22:57Z
dc.date.available	2015-06-04T16:22:57Z
dc.date.issued	2015-06-04
dc.identifier.uri	http://hdl.handle.net/11400/15088
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	Chebyshev series
dc.subject	Fuzzy dynamical models
dc.subject	Nonlinear system identification
dc.subject	Qualitative modeling
dc.subject	Self-organizing maps
dc.subject	Ποιοτική μοντελοποίηση
dc.subject	Χάρτες αυτο-οργάνωσης
dc.subject	Ασαφή δυναμικά μοντέλα
dc.subject	Ταυτοποίηση μη γραμμικού συστήματος
dc.title	Modelling of nonlinear process dynamics using Kohonen's neural networks, fuzzy systems and Chebyshev series	en
heal.type	journalArticle
heal.classification	Technology
heal.classification	Chemical technology
heal.classification	Τεχνολογία
heal.classification	Χημική τεχνολογία
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh85133147
heal.classificationURI	http://skos.um.es/unescothes/C00565
heal.classificationURI	N/A-Τεχνολογία
heal.classificationURI	N/A-Χημική τεχνολογία
heal.keywordURI	http://id.loc.gov/authorities/subjects/sh85022809
heal.identifier.secondary	DOI: 10.1016/S0098-1354(01)00785-2
heal.language	en
heal.access	campus
heal.publicationDate	2002-05-15
heal.bibliographicCitation	ALEXANDRIDIS, A.P., SIETTOS, C.I., SARIMVEIS, H.K., BOUDOUVIS, A.G. & BAFAS, G.V. (2002). Modelling of nonlinear process dynamics using Kohonen's neural networks, fuzzy systems and Chebyshev series. Computers and Chemical Engineering. [online] 26 (4-5). p. 479-486. Available from: http://www.elsevier.com/[Accessed 22/01/2002]	en
heal.abstract	This paper introduces a new systematic methodology to the problem of nonlinear system identification with the aid of neural networks, fuzzy systems and truncated Chebyshev series. The proposed methodology is of general use and results in both a linguistic and an analytical model of the system under study. The method was successfully tested in the identification of certain operating regions in a Continuous Stirred Tank Reactor (CSTR) exhibiting various types of nonlinear behaviour, such as limit cycles and multiple steady states. The performance of the methodology was evaluated via a comparison with two different identification schemes, namely a feedforward neural network and an approach based on the normal form theory.	en
heal.publisher	Elsevier	en
heal.journalName	Computers and Chemical Engineering	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	true