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dc.contributor.authorPedreño Molina, Juan Luis 
dc.contributor.authorMonzó Cabrera, Juan 
dc.contributor.authorSánchez Hernández, David Agapito 
dc.date.accessioned2010-12-14T13:42:04Z
dc.date.available2010-12-14T13:42:04Z
dc.date.issued2004-03
dc.identifier.citationPEDREÑO MOLINA, J.L., MONZÓ CABRERA, J., SÁNCHEZ HERNÁNDEZ, D. A new predictive neural architecture for solving temperature inverse problems in microwave-assisted drying processes. Neurocomputing, vol. 64 : 521-528, 2004. ISSN 0925-2312en_US
dc.description.abstractIn this paper, a novel learning architecture based on neural networks is used for temperature inverse modeling in microwave-assisted drying processes. The proposed design combines the accuracy of the radial basis functions (RBF) and the algebraic capabilities of the matrix polynomial structures by using a two-level structure. This architecture is trained by temperature curves, TcðtÞ; previously generated by a validated drying model. The interconnection of the learning-based networks has enabled the finding of electric field (E) optimal values which provide the TcðtÞ curve that best fits a desired temperature target in a specific time sloten_US
dc.formatapplication/pdf
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.titleA new predictive neural architecture for solving temperature inverse problems in microwave-assisted drying processesen_US
dc.typeinfo:eu-repo/semantics/articleen_US
dc.subject.otherTeoría de la Señal y las Comunicacionesen_US
dc.subjectLearning-based predictive systemen_US
dc.subjectElectric field estimationen_US
dc.subjectNeural network modelingen_US
dc.subjectMicrowave-assisted drying applicationsen_US
dc.subjectInverse problemen_US
dc.identifier.urihttp://hdl.handle.net/10317/1476
dc.peerreviewen_US
dc.contributor.investgroupElectromagnetismo y Materiaen_US
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess


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