کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
5497881 | 1532080 | 2016 | 7 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A comparison of back propagation and Generalized Regression Neural Networks performance in neutron spectrometry
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
فیزیک و نجوم
تشعشع
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چکیده انگلیسی
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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Applied Radiation and Isotopes - Volume 117, November 2016, Pages 20-26
Journal: Applied Radiation and Isotopes - Volume 117, November 2016, Pages 20-26
نویسندگان
Ma. del Rosario MartÃnez-Blanco, Gerardo Ornelas-Vargas, Luis Octavio SolÃs-Sánchez, Rodrigo Castañeda-Miranada, Héctor René Vega-Carrillo, José M. Celaya-Padilla, Idalia Garza-Veloz, Margarita MartÃnez-Fierro, José Manuel Ortiz-RodrÃguez,