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Telecommunications and Radio Engineering
SJR: 0.202 SNIP: 0.2 CiteScore™: 0.23

ISSN Imprimer: 0040-2508
ISSN En ligne: 1943-6009

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Telecommunications and Radio Engineering

DOI: 10.1615/TelecomRadEng.v75.i12.80
pages 1121-1128

ARTIFICIAL NEURAL NETWORKS IN GAMMA-SPECTRUM BASED RADIONUCLIDE IDENTIFICATION

Anna I. Skrypnyk
National Science Center "Kharkiv Institute of Physics and Technology"

RÉSUMÉ

Artificial neural networks are widely used to solve a variety of classification problems. In this work, we consider the possibilities to apply them in gamma-spectrum based identification of nuclides in mixed radioactive compounds. We used a combination of the Geant4 software and the Monte-Carlo method to simulate response functions of the CdZnTe detector toward the radiation from 57Co, 137Cs, 131I, 133Ba and 241Am. The next step was to find the numerical values of the CdZnTe detector response toward mixed source radiation. The obtained spectra were applied to train and validate the network. Multilayer feedforward network was constructed using the Neuroph framework. In course of the study the network architecture characteristics were improved.


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