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Communication Dans Un Congrès Année : 1995

Statistical modelling by neural networks in gamma-spectrometry

Résumé

Layered Neural Networks are a class of models based on neural computation and have been applied to the measurement of uranium enrichment. The usual methods consider a limited number of $X$- and $\gamma$-ray peaks, and require calibrated instrumentation for each sample. Since the source-detector ensemble geometry conditions critically differ between such measurements, the spectral region of interest is normally reduced to improve the accuracy of such conventional methods by focusing on the $K_{\alpha}X$ region where the three elementary components are present. Such measurements lead to the desired ratio. Experimental data have been used to study the performance of neural networks involving a Maximum-Likelihood Method. The encoding of the data by a Neural Network approach is a promising method for the measurement of uranium ${}^{235}U$ and ${}^{238}U$ in infinitely thick samples.
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Dates et versions

hal-00221531 , version 1 (01-02-2008)

Identifiants

  • HAL Id : hal-00221531 , version 1

Citer

Vincent Vigneron, Jean Morel, Marie-Christine Lepy, Jean-Marc Martinez. Statistical modelling by neural networks in gamma-spectrometry. Conference and International Symposium on Radionuclide Metrology, May 1995, Paris, France. pp.00. ⟨hal-00221531⟩
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