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
Materias
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
Recurso de información
Formato
application/pdf
Idioma
Inglés
Audiencia
Público en general
Repositorio Orígen
Repositorio Institucional Caxcán
Descargas
0