View in EDS

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

Saved in:
Bibliographic Details
Title: A comparison of back propagation and Generalized Regression Neural Networks performance in neutron spectrometry.
Authors: Martínez-Blanco, Ma. del Rosario1,2,3,4 mrosariomb@yahoo.com.mx, Ornelas-Vargas, Gerardo1,2,3,4 ornelashlls@gmail.com, Solís-Sánchez, Luis Octavio1,2,3,4 l_solis@ymail.com, Castañeda-Miranada, Rodrigo1,2,3,4 rcastm@gmail.com, Vega-Carrillo, Héctor René2,3,4,5 fermineutron@yahoo.com, Celaya-Padilla, José M.1,3 jose.cpadilla@gmail.com, Garza-Veloz, Idalia1,2,3,6 idgarve@gmail.com, Martínez-Fierro, Margarita1,2,3,6 margaritamf@uaz.edu.mx, Ortiz-Rodríguez, José Manuel1,2,3,4 morvymm@yahoo.com.mx
Source: Applied Radiation & Isotopes. Nov2016, Vol. 117, p20-26. 7p.
Subjects: Back propagation, Artificial neural networks, Regression analysis, Neutron spectroscopy, Radiation dosimetry
Abstract: 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. [ABSTRACT FROM AUTHOR]
Copyright of Applied Radiation & Isotopes is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
Description
Abstract: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. [ABSTRACT FROM AUTHOR]
ISSN:09698043
DOI:10.1016/j.apradiso.2016.04.011