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Título: Performance of artificial neural networks and genetical evolved artificial neural networks unfolding techniques
Palabras clave: Neutron spectrometry
neural networks
evolutive algorithms
Fecha de publicación: 31-Jul-2012
Editorial: Revista mexicana de física
Descripción: 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.
Other Identifiers: http://www.scielo.org.mx/scielo.php?script=sci_arttext&pid=S0035-001X2011000700020
Aparece en las Colecciones:Revista Mexicana de Física

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