Please use this identifier to cite or link to this item: http://ricaxcan.uaz.edu.mx/jspui/handle/20.500.11845/1455
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dc.contributor241916es_ES
dc.contributor172896es_ES
dc.contributor172879es_ES
dc.contributor123645es_ES
dc.contributor6207es_ES
dc.contributor268446es_ES
dc.contributor49237es_ES
dc.contributor200970es_ES
dc.contributor.otherhttps://orcid.org/0000-0002-7081-9084es_ES
dc.coverage.spatialGlobales_ES
dc.creatorMartínez Blanco, María del Rosario-
dc.creatorOrnelas Vargas, Gerardo-
dc.creatorSolís Sánchez, Luis Octavio-
dc.creatorCastañeda Miranda, Rodrígo-
dc.creatorVega Carrillo, Héctor René-
dc.creatorCelaya Padilla, José María-
dc.creatorGarza Veloz, Idalia-
dc.creatorMartínez Fierro, Margarita de la Luz-
dc.creatorOrtíz Rodríguez, José Manuel-
dc.date.accessioned2020-03-24T20:20:30Z-
dc.date.available2020-03-24T20:20:30Z-
dc.date.issued2016-04-19-
dc.identifierinfo:eu-repo/semantics/publishedVersiones_ES
dc.identifier.issn0969-8043es_ES
dc.identifier.urihttp://ricaxcan.uaz.edu.mx/jspui/handle/20.500.11845/1455-
dc.identifier.urihttps://doi.org/10.48779/7yje-tj22es_ES
dc.description.abstractThe process of unfolding the neutron energy spectrum has been subject of research for many years. Monte Carlo, iterative methods, the bayesian theory, the principle of maximum entropy are some of the methods used. The drawbacks associated with traditional unfolding procedures have motivated the research of complementary approaches. Back Propagation Neural Networks (BPNN), have been applied with success in neutron spectrometry and dosimetry domains, however, the structure and learning parameters are factors that highly impact in the networks performance. In ANN domain, Generalized Regression Neural Network (GRNN) is one of the simplest neural networks in term of network architecture and learning algorithm. The learning is instantaneous, requiring no time for training. Opposite to BPNN, a GRNN would be formed instantly with just a 1-pass training on the development data. In the network development phase, the only hurdle is to optimize the hyper-parameter, which is known as sigma, governing the smoothness of the network. The aim of this work was to compare the performance of BPNN and GRNN in the solution of the neutron spectrometry problem. From results obtained it can be observed that despite the very similar results, GRNN performs better than BPNN.es_ES
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.relationhttp://dx.doi.org/10.1016/j.apradiso.2016.04.011es_ES
dc.relation.urigeneralPublices_ES
dc.rightsAtribución-NoComercial-CompartirIgual 3.0 Estados Unidos de América*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/us/*
dc.sourceApplied Radiation and Isotopes Vol. 117, , Pages 20-26es_ES
dc.subject.classificationINGENIERIA Y TECNOLOGIA [7]es_ES
dc.subject.otherNeutron spectrometryes_ES
dc.subject.otherArtificial Neural Networkses_ES
dc.subject.otherUnfoldinges_ES
dc.subject.otherComparisones_ES
dc.titleA comparison of back propagation and Generalized Regression Neural Networks performance in neutron spectrometryes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
Appears in Collections:*Documentos Académicos*-- Doc. en Ing. y Tec. Aplicada

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