Título

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

Autor

MA. DEL ROSARIO MARTINEZ BLANCO

GERARDO ORNELAS VARGAS

LUIS OCTAVIO SOLIS SANCHEZ

RODRIGO CASTAÑEDA MIRANDA

HECTOR RENE VEGA CARRILLO

IDALIA GARZA VELOZ

MARGARITA DE LA LUZ MARTINEZ FIERRO

JOSE MANUEL ORTIZ RODRIGUEZ

Nivel de Acceso

Acceso Abierto

Resumen o descripción

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.

Producción Científica de la Universidad Autónoma de Zacatecas UAZ

Fecha de publicación

19 de abril de 2016

Tipo de publicación

Artículo

Formato

application/pdf

Idioma

Inglés

Audiencia

Público en general

Repositorio Orígen

Repositorio Institucional Caxcán

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