Model predictive control for distributed parameter systems using rbf neural networks

Aggelogiannaki, E; Sarimveis, H

dc.contributor.author	Aggelogiannaki, E	en
dc.contributor.author	Sarimveis, H	en
dc.date.accessioned	2014-03-01T02:49:57Z
dc.date.available	2014-03-01T02:49:57Z
dc.date.issued	2005	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/34818
dc.subject	Control Problem	en
dc.subject	Distributed Parameter System	en
dc.subject	Model Predictive Control	en
dc.subject	Non-linear Model	en
dc.subject	Predictive Control	en
dc.subject	rbf neural network	en
dc.subject	Temperature Distribution	en
dc.subject	Neural Network	en
dc.subject	Neural Network Model	en
dc.subject	Radial Basis Function Neural Network	en
dc.title	Model predictive control for distributed parameter systems using rbf neural networks	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1007/978-1-4020-5626-0_5	en
heal.identifier.secondary	http://dx.doi.org/10.1007/978-1-4020-5626-0_5	en
heal.publicationDate	2005	en
heal.abstract	A new approach for the identification and control of distributed parameter systems is presented in this paper. A radial basis neural network is used to model the distribution of the system output variables over space and time. The neural network model is then used for synthesizing a non linear model predictive control configuration. The resulting framework is particular useful for	en
heal.journalName	International Conference on Informatics in Control, Automation and Robotics	en
dc.identifier.doi	10.1007/978-1-4020-5626-0_5	en