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| Format: | Artículo científico |
| Language: | en |
| Published: |
Sociedad Mexicana de Física A.C.
2008
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| Online Access: | https://www.redalyc.org/articulo.oa?id=57054107 |
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Table of Contents:
- Learning limits of an artificial neural network J.J. Vega R. Reynoso H. Carrillo Calvet Física, Astronomía y Matemáticas digital pulse shape analysis Neural networks pattern identification Bragg curve spectroscopy Technological advances in hardware as well as new computational paradigms give us the opportunity to apply digital techniques to PulseShape Analysis (PSA), requiring powerful resources. In this paper, we present a PSA application based on Artificial Neural Networks(ANNs). These adaptive systems offer several advantages for these tasks; nevertheless it is necessary to face the particular problems linked tothem as: the selection of the learning rule and the ANN architecture, the sizes of the training and validation data sets, overtraining, the effectof noise on the pattern identification ability, etc. We will present evidences of the effect on the performance of a back-propagation ANN as apattern identifier of both: the size of the noise that the Bragg curve spectrometer signal present and of overtraining. In fact, these two effects are related 2008 artículo científico 0035-001X https://www.redalyc.org/articulo.oa?id=57054107 en http://www.redalyc.org/revista.oa?id=570 Revista Mexicana de Física application/pdf Sociedad Mexicana de Física A.C. Revista Mexicana de Física (México) Num.1 Vol.54