A neural network approach for modeling and control of continuous digesters

dc.contributor.author	Αλεξανδρίδης, Αλέξανδρος Π.	el
dc.contributor.author	Σαρίμβεης, Χαράλαμπος Κ.	el
dc.contributor.author	Μπάφας, Γιώργος Β.	el
dc.contributor.author	Ρετσινά, Θεοδώρα Ρ.	el
dc.date.accessioned	2015-06-04T15:15:04Z
dc.date.issued	2015-06-04
dc.identifier.uri	http://hdl.handle.net/11400/15076
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.tappi.org/	en
dc.subject	Identification
dc.subject	Predictive control systems
dc.subject	Neural networks
dc.subject	ταυτοποίηση
dc.subject	Προγνωστικά συστήματα ελέγχου
dc.subject	Νευρωνικά δίκτυα
dc.title	A neural network approach for modeling and control of continuous digesters	en
heal.type	conferenceItem
heal.classification	Technology
heal.classification	Electrical engineering
heal.classification	Τεχνολογία
heal.classification	Ηλεκτρολογία Μηχανολογία
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh85133147
heal.classificationURI	http://zbw.eu/stw/descriptor/18426-4
heal.classificationURI	N/A-Τεχνολογία
heal.classificationURI	N/A-Ηλεκτρολογία Μηχανολογία
heal.keywordURI	http://zbw.eu/stw/descriptor/19808-6
heal.identifier.secondary	ISBN: 193065796X
heal.dateAvailable	10000-01-01
heal.language	en
heal.access	forever
heal.publicationDate	2002
heal.bibliographicCitation	Alexandridis, A.P., Sarimveis, H.K., Bafas, G.V. & Retsina, T.R. (2002) A neural network approach for modeling and control of continuous digesters, In: Proceedings of the 2002 TAPPI Fall Technical Conference and Trade Fair. San Diego, CA, United States. 8-11 September, 2002. [online]. p. 355-367. Available from: http://www.tappi.org/	en
heal.abstract	In this paper we present a methodology for developing dynamical models for continuous digesters using input-output data. The methodology uses the Radial Basis Function (RBF) neural network architecture, which is continuously increasing its popularity in solving system identification problems, due its simple network structure and the short training times it employs. The produced dynamic RBF network model can be utilized to predict the future behavior of the process, analyze the dynamics of the digester and control the process through a Model Predictive Control (MPC) scheme.	en
heal.publisher	TAPPI	en
heal.fullTextAvailability	false
heal.conferenceName	2002 TAPPI Fall Technical Conference and Trade Fair	el
heal.conferenceItemType	poster