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Title: Generalized Regression Neural Networks with Application in Neutron Spectrometry
Authors: Martínez Blanco, María del Rosario
Castañeda Miranda, Víctor Hugo
Ornelas Vargas, Gerardo
Guerrero Osuna, Héctor Alonso
Solís Sánchez, Luis Octavio
Castañeda Miranda, Rodrígo
Celaya Padilla, José María
Galván Tejada, Carlos Eric
Galván Tejada, Jorge Issac
Vega Carrillo, Héctor René
Martínez Fierro, Margarita de la Luz
Garza Veloz, Idalia
Ortíz Rodríguez, José Manuel
Issue Date: 19-Oct-2016
Publisher: Universidad de Sao Paulo, Brasil
Abstract: The 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.
URI: http://localhost/xmlui/handle/20.500.11845/754
ISBN: 978-953-51-2705-5
Other Identifiers: info:eu-repo/semantics/publishedVersion
Appears in Collections:*Documentos Académicos*-- UA Ciencias Nucleares

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