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

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 y 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.

Descripción

Título: Performance of artificial neural networks and genetical evolved artificial neural networks unfolding techniques
Autor/es:
  • 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
Tipo de Documento: Artículo
Título de Revista/Publicación: Revista Mexicana de Fisica S
Fecha: 2011
Volumen: 57
Materias:
Palabras Clave Informales: Neutron spectrometry; neural networks; evolutive algorithms
Escuela: E.T.S.I. Industriales (UPM)
Departamento: Ingeniería Nuclear [hasta 2014]
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

Texto completo

[img]
Vista Previa
PDF (Document Portable Format) - Se necesita un visor de ficheros PDF, como GSview, Xpdf o Adobe Acrobat Reader
Descargar (1MB) | Vista Previa

Resumen

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.

Más información

ID de Registro: 15263
Identificador DC: http://oa.upm.es/15263/
Identificador OAI: oai:oa.upm.es:15263
Depositado por: Memoria Investigacion
Depositado el: 30 Nov 2013 13:58
Ultima Modificación: 21 Abr 2016 15:18
  • Open Access
  • Open Access
  • Sherpa-Romeo
    Compruebe si la revista anglosajona en la que ha publicado un artículo permite también su publicación en abierto.
  • Dulcinea
    Compruebe si la revista española en la que ha publicado un artículo permite también su publicación en abierto.
  • Recolecta
  • e-ciencia
  • Observatorio I+D+i UPM
  • OpenCourseWare UPM