Performance of artificial neural networks and genetical evolved artificial neural networks unfolding techniques

Ortiz Rodríguez, José Manuel and Martínez Blanco, María del Rosario and Vega-Carrillo, Héctor René and Gallego Díaz, Eduardo F. and Lorente Fillol, Alfredo and Méndez Villafañe, Roberto and Los Arcos Merino, José María and Guerrero Araque, Jorge Enrique (2011). Performance of artificial neural networks and genetical evolved artificial neural networks unfolding techniques. "Revista Mexicana de Fisica S", v. 57 (n. 1); pp. 89-92. ISSN 0035-001X.

Description

Title: Performance of artificial neural networks and genetical evolved artificial neural networks unfolding techniques
Author/s:
  • Ortiz Rodríguez, José Manuel
  • Martínez Blanco, María del Rosario
  • Vega-Carrillo, Héctor René
  • Gallego Díaz, Eduardo F.
  • Lorente Fillol, Alfredo
  • Méndez Villafañe, Roberto
  • Los Arcos Merino, José María
  • Guerrero Araque, Jorge Enrique
Item Type: Article
Título de Revista/Publicación: Revista Mexicana de Fisica S
Date: 2011
ISSN: 0035-001X
Volume: 57
Subjects:
Freetext Keywords: Neutron spectrometry; neural networks; evolutive algorithms
Faculty: E.T.S.I. Industriales (UPM)
Department: Ingeniería Nuclear [hasta 2014]
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

With the Bonner spheres spectrometer neutron spectrum is obtained through an unfolding procedure. Monte Carlo methods, Regularization, Parametrization, Least-squares, and Maximum Entropy are some of the techniques utilized for unfolding. In the last decade methods based on Artificial Intelligence Technology have been used. Approaches based on Genetic Algorithms and Artificial Neural Networks have been developed in order to overcome the drawbacks of previous techniques. Nevertheless the advantages of Artificial Neural Networks still it has some drawbacks mainly in the design process of the network, vg the optimum selection of the architectural and learning ANN parameters. In recent years the use of hybrid technologies, combining Artificial Neural Networks and Genetic Algorithms, has been utilized to. In this work, several ANN topologies were trained and tested using Artificial Neural Networks and Genetically Evolved Artificial Neural Networks in the aim to unfold neutron spectra using the count rates of a Bonner sphere spectrometer. Here, a comparative study of both procedures has been carried out.

More information

Item ID: 15263
DC Identifier: http://oa.upm.es/15263/
OAI Identifier: oai:oa.upm.es:15263
Deposited by: Memoria Investigacion
Deposited on: 30 Nov 2013 13:58
Last Modified: 21 Apr 2016 15:18
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