Affine Radial Basis Function Neural Networks for the Robust Control of Hyperbolic Distributed Parameter Systems

Aggelogiannaki, E; Sarimveis, H

dc.contributor.author	Aggelogiannaki, E	en
dc.contributor.author	Sarimveis, H	en
dc.date.accessioned	2014-03-01T01:54:50Z
dc.date.available	2014-03-01T01:54:50Z
dc.date.issued	2006	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/27492
dc.relation.uri	http://www.akademik.unsri.ac.id/download/journal/files/waset/v1-2-17-6.pdf	en
dc.relation.uri	http://www.waset.org/journals/waset/v22/v22-29.pdf	en
dc.subject	Distributed Parameter System	en
dc.subject	Empirical Model	en
dc.subject	Nonlinear Model	en
dc.subject	Partial Differential Equation	en
dc.subject	Radial Basis Function	en
dc.subject	rbf neural network	en
dc.subject	Robust Control	en
dc.subject	State Space	en
dc.subject	Temperature Distribution	en
dc.subject	Input Output	en
dc.subject	Radial Basis Function Neural Network	en
dc.title	Affine Radial Basis Function Neural Networks for the Robust Control of Hyperbolic Distributed Parameter Systems	en
heal.type	journalArticle	en
heal.publicationDate	2006	en
heal.abstract	In this work, a radial basis function (RBF) neural network is developed for the identification of hyperbolic distributed parameter systems (DPSs). This empirical model is based only on process input-output data and used for the estimation of the controlled variables at specific locations, without the need of online solution of partial differential equations (PDEs). The nonlinear model that is obtained	en