A comparison of back propagation and Generalized Regression Neural Networks performance in neutron spectrometry

详细信息    查看全文

• 作者：Ma. del Rosario Mart&iacute ; nez-Blanco^a ; ^b ; ^c ; ^d ; ^{mrosariomb@yahoo.com.mx" class="auth_mail" title="E-mail the corresponding author} ; Gerardo Ornelas-Vargas^a ; ^b ; ^c ; ^d ; ^{ornelashlls@gmail.com" class="auth_mail" title="E-mail the corresponding author} ; Luis Octavio Sol&iacute ; s-Sá ; nchez^a ; ^b ; ^c ; ^d ; ^{l_solis@ymail.com" class="auth_mail" title="E-mail the corresponding author} ; Rodrigo Castañ ; eda-Miranada^a ; ^b ; ^c ; ^d ; ^{rcastm@gmail.com" class="auth_mail" title="E-mail the corresponding author} ; Hé ; ctor René ; Vega-Carrillo^b ; ^c ; ^d ; ^e ; ^{fermineutron@yahoo.com" class="auth_mail" title="E-mail the corresponding author} ; José ; M. Celaya-Padilla^a ; ^c ; ^{jose.cpadilla@gmail.com" class="auth_mail" title="E-mail the corresponding author} ; Idalia Garza-Veloz^a ; ^b ; ^c ; ^f ; ^{idgarve@gmail.com" class="auth_mail" title="E-mail the corresponding author} ; Margarita Mart&iacute ; nez-Fierro^a ; ^b ; ^c ; ^f ; ^{margaritamf@uaz.edu.mx" class="auth_mail" title="E-mail the corresponding author} ; José ; Manuel Ortiz-Rodr&iacute ; guez^a ; ^b ; ^c ; ^d ; ^{morvymm@yahoo.com.mx" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Neutron spectrometry ; Artificial Neural Networks ; Unfolding ; Comparison
• 刊名：Applied Radiation and Isotopes
• 出版年：2016
• 出版时间：November 2016
• 年：2016
• 卷：117
• 期：Complete
• 页码：20-26
• 全文大小：1060 K

文摘

The 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.