Please use this identifier to cite or link to this item: http://ricaxcan.uaz.edu.mx/jspui/handle/20.500.11845/1456
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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.contributor.otherhttps://orcid.org/0000-0003-2545-4116-
dc.coverage.spatialGlobales_ES
dc.creatorMartinez Blanco, María del Rosario-
dc.creatorOrnelas Vargas, Gerardo-
dc.creatorCastañeda Miranda, Celina Lizeth-
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.accessioned2020-03-24T20:21:12Z-
dc.date.available2020-03-24T20:21:12Z-
dc.date.issued2016-04-30-
dc.identifierinfo:eu-repo/semantics/publishedVersiones_ES
dc.identifier.issn0969-8043es_ES
dc.identifier.urihttp://ricaxcan.uaz.edu.mx/jspui/handle/20.500.11845/1456-
dc.identifier.urihttps://doi.org/10.48779/5zy6-dr62es_ES
dc.description.abstractThe most delicate part of neutron spectrometry, is the unfolding process. The derivation of the spectral information is not simple because the unknown is not given directly as a result of the measurements. Novel methods based on Artificial Neural Networks have been widely investigated. In prior works, back propagation neural networks (BPNN) have been used to solve the neutron spectrometry problem, however, some drawbacks still exist using this kind of neural nets, i.e. the optimum selection of the network topology and the long training time. Compared to BPNN, it's usually much faster to train a generalized regression neural network (GRNN). That's mainly because spread constant is the only parameter used in GRNN. Another feature is that the network will converge to a global minimum, provided that the optimal values of spread has been determined and that the dataset adequately represents the problem space. In addition, GRNN are often more accurate than BPNN in the prediction. These characteristics make GRNNs to be of great interest in the neutron spectrometry domain. This work presents a computational tool based on GRNN capable to solve the neutron spectrometry problem. This computational code, automates the pre-processing, training and testing stages using a k-fold cross validation of 3 folds, the statistical analysis and the post-processing of the information, using 7 Bonner spheres rate counts as only entrance data. The code was designed for a Bonner Spheres System based on a LiI(Eu) neutron detector and a response matrix expressed in 60 energy bins taken from an International Atomic Energy Agency compilation.es_ES
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.relationhttp://dx.doi.org/10.1016/j.apradiso.2016.04.029es_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, pp. 8-14.es_ES
dc.subject.classificationINGENIERIA Y TECNOLOGIA [7]es_ES
dc.subject.otherArtificial neural networkses_ES
dc.subject.otherNeutron spectrometryes_ES
dc.subject.otherBonner sphereses_ES
dc.subject.otherUnfoldinges_ES
dc.subject.otherGRNN architecturees_ES
dc.titleA neutron spectrum unfolding code based on generalized regression artificial neural networkses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
Appears in Collections:*Documentos Académicos*-- Doc. en Ing. y Tec. Aplicada

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