Adaptive control of continuous pulp digesters based on radial basis function neural network models

Alexandridis, A; Sarimveis, H; Bafas, G

dc.contributor.author	Alexandridis, A	en
dc.contributor.author	Sarimveis, H	en
dc.contributor.author	Bafas, G	en
dc.date.accessioned	2014-03-01T01:18:35Z
dc.date.available	2014-03-01T01:18:35Z
dc.date.issued	2003	en
dc.identifier.issn	15707946	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/15102
dc.subject	Adaptive Control	en
dc.subject	Fuzzy Clustering	en
dc.subject	Nonlinear Model Predictive Control	en
dc.subject	Radial Basis Function	en
dc.subject	rbf network	en
dc.subject	Radial Basis Function Neural Network	en
dc.subject	Time Varying	en
dc.title	Adaptive control of continuous pulp digesters based on radial basis function neural network models	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/S1570-7946(03)80247-X	en
heal.identifier.secondary	http://dx.doi.org/10.1016/S1570-7946(03)80247-X	en
heal.publicationDate	2003	en
heal.abstract	In this paper, an adaptive nonlinear Model Predictive Control (MPC) configuration is proposed, where the model used for predicting the future behavior of the process is a discrete dynamic Radial Basis Function (RBF) network. The innovative fuzzy clustering algorithm allows the continuous and easy adaptation of the model, thus making it suitable for controlling time-varying processes, such as the continuous pulp digester. © 2003 Elsevier B.V. All rights reserved.	en
heal.journalName	Computer Aided Chemical Engineering	en
dc.identifier.doi	10.1016/S1570-7946(03)80247-X	en
dc.identifier.volume	14	en
dc.identifier.issue	C	en
dc.identifier.spage	995	en
dc.identifier.epage	1000	en