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A neural network approach for modeling and control of continuous digesters

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dc.contributor.author Alexandridis, A en
dc.contributor.author Sarimveis, H en
dc.contributor.author Bafas, G en
dc.contributor.author Retsina, T en
dc.date.accessioned 2014-03-01T02:49:12Z
dc.date.available 2014-03-01T02:49:12Z
dc.date.issued 2002 en
dc.identifier.uri https://dspace.lib.ntua.gr/xmlui/handle/123456789/34398
dc.relation.uri http://www.scopus.com/inward/record.url?eid=2-s2.0-0142156242&partnerID=40&md5=3fd6d043b649a8f7177bf6ab38f76a44 en
dc.subject.other Identification (control systems) en
dc.subject.other Predictive control systems en
dc.subject.other Radial basis function networks en
dc.subject.other Model predictive control (MPC) scheme en
dc.subject.other Pulp digesters en
dc.subject.other Neural Networks en
dc.subject.other Pulps en
dc.title A neural network approach for modeling and control of continuous digesters en
heal.type conferenceItem en
heal.publicationDate 2002 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.journalName TAPPI Fall technical Conference and Trade Fair en
dc.identifier.spage 355 en
dc.identifier.epage 367 en


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