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Neural networks in virtual reference tuning

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Neural networks in virtual reference tuning

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dc.contributor.author Esparza Peidro, Alicia es_ES
dc.contributor.author Sala, Antonio es_ES
dc.contributor.author Albertos Pérez, Pedro es_ES
dc.date.accessioned 2016-07-06T07:40:39Z
dc.date.available 2016-07-06T07:40:39Z
dc.date.issued 2011-09
dc.identifier.issn 0952-1976
dc.identifier.uri http://hdl.handle.net/10251/67195
dc.description.abstract This paper discusses the application of the virtual reference tuning (VRT) techniques to tune neural controllers from batch inputoutput data, by particularising nonlinear VRT and suitably computing gradients backpropagating in time. The flexibility of gradient computation with neural networks also allows alternative block diagrams with extra inputs to be considered. The neural approach to VRT in a closed-loop setup is compared to the linear VRFT one in a simulated crane example. © 2011 Elsevier Ltd. All rights reserved. es_ES
dc.description.sponsorship A. Esparza is grateful to the project GVPRE/2008/116 financed by Generalitat Valenciana. The authors are also grateful to the financial support of Grants dpi2008-06731-c02-01, dpi2011-27845-c02-01 (Spanish Government) and prometeo/2008/088 (Generalitat Valenciana). en_EN
dc.language Español es_ES
dc.publisher Elsevier es_ES
dc.relation.ispartof Engineering Applications of Artificial Intelligence es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Back propagation through time es_ES
dc.subject Data-based controller tuning es_ES
dc.subject Direct controller design es_ES
dc.subject Model reference control es_ES
dc.subject Neural networks es_ES
dc.subject Virtual reference feedback tuning es_ES
dc.subject Back-propagation through time es_ES
dc.subject Controllers es_ES
dc.subject Feedback es_ES
dc.subject.classification INGENIERIA DE SISTEMAS Y AUTOMATICA es_ES
dc.title Neural networks in virtual reference tuning es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1016/j.engappai.2011.04.003
dc.relation.projectID info:eu-repo/grantAgreement/GVA//GVPRE%2F2008%2F116/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MICINN//DPI2011-27845-C02-01/ES/ESTIMACION, OPTIMIZACION Y CONTROL MULTIVARIABLE EN SISTEMAS MULTI-MODELO/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MICINN//DPI2008-06731-C02-01/ES/IDENTIFICACION Y CONTROL DE SISTEMAS NO LINEALES Y LTV MEDIANTE MULTI-MODELOS TAKAGI-SUGENO Y POLINOMIALES/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería de Sistemas y Automática - Departament d'Enginyeria de Sistemes i Automàtica es_ES
dc.description.bibliographicCitation Esparza Peidro, A.; Sala, A.; Albertos Pérez, P. (2011). Neural networks in virtual reference tuning. Engineering Applications of Artificial Intelligence. 24(6):983-995. https://doi.org/10.1016/j.engappai.2011.04.003 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://dx.doi.org/10.1016/j.engappai.2011.04.003 es_ES
dc.description.upvformatpinicio 983 es_ES
dc.description.upvformatpfin 995 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 24 es_ES
dc.description.issue 6 es_ES
dc.relation.senia 41399 es_ES
dc.contributor.funder Ministerio de Ciencia e Innovación es_ES
dc.contributor.funder Generalitat Valenciana es_ES


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