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

Alexandridis, AP; Siettos, CI; Sarimveis, HK; Boudouvis, AG; Bafas, GV

dc.contributor.author	Alexandridis, AP	en
dc.contributor.author	Siettos, CI	en
dc.contributor.author	Sarimveis, HK	en
dc.contributor.author	Boudouvis, AG	en
dc.contributor.author	Bafas, GV	en
dc.date.accessioned	2014-03-01T02:42:08Z
dc.date.available	2014-03-01T02:42:08Z
dc.date.issued	2002	en
dc.identifier.issn	0098-1354	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/30804
dc.subject	Chebyshev series	en
dc.subject	Fuzzy dynamical models	en
dc.subject	Nonlinear system identification	en
dc.subject	Qualitative modeling	en
dc.subject	Self-organizing maps	en
dc.subject.classification	Computer Science, Interdisciplinary Applications	en
dc.subject.classification	Engineering, Chemical	en
dc.subject.other	Chebyshev approximation	en
dc.subject.other	Feedforward neural networks	en
dc.subject.other	Nonlinear systems	en
dc.subject.other	Performance	en
dc.subject.other	Steady states	en
dc.subject.other	Chemical engineering	en
dc.subject.other	dynamic modeling	en
dc.subject.other	fuzzy mathematics	en
dc.subject.other	neural network	en
dc.subject.other	process industry	en
dc.subject.other	analytic method	en
dc.subject.other	artificial neural network	en
dc.subject.other	chebyshev series	en
dc.subject.other	conference paper	en
dc.subject.other	continuous stirred tank reactor	en
dc.subject.other	diagnostic approach route	en
dc.subject.other	evaluation	en
dc.subject.other	intermethod comparison	en
dc.subject.other	linguistics	en
dc.subject.other	mathematical model	en
dc.subject.other	methodology	en
dc.subject.other	nonlinear system	en
dc.subject.other	steady state	en
dc.title	Modelling of nonlinear process dynamics using Kohonen's neural networks, fuzzy systems and Chebyshev series	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1016/S0098-1354(01)00785-2	en
heal.identifier.secondary	http://dx.doi.org/10.1016/S0098-1354(01)00785-2	en
heal.language	English	en
heal.publicationDate	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. (C) 2002 Elsevier Science 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(01)00785-2	en
dc.identifier.isi	ISI:000175994800002	en
dc.identifier.volume	26	en
dc.identifier.issue	4-5	en
dc.identifier.spage	479	en
dc.identifier.epage	486	en