dc.contributorhttps://orcid.org/0000-0002-7081-9084
dc.contributorhttps://orcid.org/0000-0003-2545-4116
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.created2020-03-24T20:20:30Z
dc.date.issued2016-04-19
dc.identifier0969-8043
dc.identifierhttp://ricaxcan.uaz.edu.mx/jspui/handle/20.500.11845/1455
dc.identifierhttps://doi.org/10.48779/7yje-tj22
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.
dc.languageeng
dc.publisherElsevier
dc.relationgeneralPublic
dc.relationhttp://dx.doi.org/10.1016/j.apradiso.2016.04.011
dc.rightshttp://creativecommons.org/licenses/by-nc-sa/3.0/us/
dc.rightsAtribución-NoComercial-CompartirIgual 3.0 Estados Unidos de América
dc.sourceApplied Radiation and Isotopes Vol. 117, , Pages 20-26
dc.titleA comparison of back propagation and Generalized Regression Neural Networks performance in neutron spectrometry
dc.typeinfo:eu-repo/semantics/article


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