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Título : A neutron spectrum unfolding code based on generalized regression artificial neural networks
Autor : 241916
172896
172879
123645
6207
 268446
49237
200970
Fecha de publicación : 30-abr-2016
Editorial : Elsevier
Resumen : The 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.
URI : http://ricaxcan.uaz.edu.mx/jspui/handle/20.500.11845/1456
ISSN : 0969-8043
Otros identificadores : info:eu-repo/semantics/publishedVersion
Aparece en las colecciones: *Documentos Académicos*-- Doc. en Ing. y Tec. Aplicada

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