A neutron spectrum unfolding code based on generalized regression artificial neural networks. (November 2016)

• Record Type: Journal Article
• Authors: del Rosario Martinez-Blanco, Ma.; Ornelas-Vargas, Gerardo; Castañeda-Miranda, Celina Lizeth; Solís-Sánchez, Luis Octavio; Castañeda-Miranada, Rodrigo; Vega-Carrillo, Héctor René; Celaya-Padilla, Jose M; Garza-Veloz, Idalia; Martínez-Fierro, Margarita; Ortiz-Rodríguez, José Manuel
• Is Part Of: Applied radiation and isotopes. Volume 117(2016:Nov.)
• Page Start: 8
• Page End: 14
• Publication Date: 2016-11
• Subjects: BPNN back propagation neural networks -- GRNN generalized regression neural network -- BSS Bonner Spheres System -- ANN Artificial neural networks -- PNN Probabilistic neural networks -- RBF Radial Basis Function -- IAEA International Atomic Energy Agency -- MSE mean square error; Artificial neural networks -- Neutron spectrometry -- Bonner spheres -- Unfolding -- GRNN architecture; Radiology -- Periodicals; Radiation -- Industrial applications -- Periodicals; Nuclear chemistry -- Periodicals; Internet resource; Periodical; 660.298
• DOI: 10.1016/j.apradiso.2016.04.029
• Languages: English
• ISSNs: 0969-8043

Abstract

Abstract: 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 6 LiI(Eu) neutron detector and a response matrix expressed in 60 energy bins taken from an International Atomic Energy Agency compilation. Highlights: Main drawback of neutron spectrometry with BPNN is network topology optimization. Compared to BPNN, it's usually much faster to train a (GRNN). GRNN are often more accurate than BPNN in the prediction. These characteristics make GRNNs to be of great interest. This computational code, automates the pre-processing, training and testing stages.