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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


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Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες Except where otherwise noted, this item's license is described as Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες