Please use this identifier to cite or link to this item: http://ricaxcan.uaz.edu.mx/jspui/handle/20.500.11845/754
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dc.contributor6207es_ES
dc.contributor.otherhttps://orcid.org/0000-0002-7081-9084es_ES
dc.contributor.otherhttps://orcid.org/0000-0002-7635-4687-
dc.contributor.otherhttps://orcid.org/0000-0003-2545-4116-
dc.coverage.spatialGlobales_ES
dc.creatorMartínez Blanco, María del Rosario-
dc.creatorCastañeda Miranda, Víctor Hugo-
dc.creatorOrnelas Vargas, Gerardo-
dc.creatorGuerrero Osuna, Héctor Alonso-
dc.creatorSolís Sánchez, Luis Octavio-
dc.creatorCastañeda Miranda, Rodrígo-
dc.creatorCelaya Padilla, José María-
dc.creatorGalván Tejada, Carlos Eric-
dc.creatorGalván Tejada, Jorge Issac-
dc.creatorVega Carrillo, Héctor René-
dc.creatorMartínez Fierro, Margarita de la Luz-
dc.creatorGarza Veloz, Idalia-
dc.creatorOrtíz Rodríguez, José Manuel-
dc.date.accessioned2019-03-14T18:17:47Z-
dc.date.available2019-03-14T18:17:47Z-
dc.date.issued2016-10-19-
dc.identifierinfo:eu-repo/semantics/publishedVersiones_ES
dc.identifier.isbn978-953-51-2705-5es_ES
dc.identifier.isbn978-953-51-2704-8es_ES
dc.identifier.urihttp://localhost/xmlui/handle/20.500.11845/754-
dc.identifier.urihttps://doi.org/10.48779/erq8-ev17es_ES
dc.description.abstractThe aim of this research was to apply a generalized regression neural network (GRNN) to predict neutron spectrum using the rates count coming from a Bonner spheres system as the only piece of information. In the training and testing stages, a data set of 251 different types of neutron spectra, taken from the International Atomic Energy Agency compilation, were used. Fifty-one predicted spectra were analyzed at testing stage. Training and testing of GRNN were carried out in the MATLAB environment by means of a scientific and technological tool designed based on GRNN technology, which is capable of solving the neutron spectrometry problem with high performance and generalization capability. This computational tool automates the pre-processing of information, the training and testing stages, the statistical analysis, and the postprocessing of the information. In this work, the performance of feed-forward backpropagation neural networks (FFBPNN) and GRNN was compared in the solution of the neutron spectrometry problem. From the results obtained, it can be observed thatdespite very similar results, GRNN performs better than FFBPNN because the former could be used as an alternative procedure in neutron spectrum unfolding methodologies with high performance and accuracy.es_ES
dc.language.isoenges_ES
dc.publisherUniversidad de Sao Paulo, Brasiles_ES
dc.relationhttps://www.intechopen.com/books/artificial-neural-networks-models-and-applications/generalized-regression-neural-networks-with-application-in-neutron-spectrometryes_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.sourceArtificial Neural Networks; Joao Luis Garcia Rosa, p. 49-83es_ES
dc.subject.classificationCIENCIAS FISICO MATEMATICAS Y CIENCIAS DE LA TIERRA [1]es_ES
dc.subject.otherartificial intelligencees_ES
dc.subject.otherstatistical artificial neural networkses_ES
dc.subject.otherneutron spectrometryes_ES
dc.subject.otherunfolding codeses_ES
dc.subject.otherspectra unfoldinges_ES
dc.titleGeneralized Regression Neural Networks with Application in Neutron Spectrometryes_ES
dc.typeinfo:eu-repo/semantics/bookPartes_ES
Appears in Collections:*Documentos Académicos*-- UA Ciencias Nucleares

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