Combined application of Monte Carlo method and neural networks to simulate qualitative prompt gamma neutron activation analysis
Prompt gamma spectrum produced by thermal neutron absorption and fast neutron inelastic scattering is simulated using Monte Carlo code MCNP4C. The simulated spectrum is analyzed with artificial neural network techniques. The neural network in our study is trained based on back-propagation algorithm...
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| Veröffentlicht in: | Journal of radioanalytical and nuclear chemistry 2010-02, Vol.283 (2), p.403-407 |
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| container_end_page | 407 |
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| container_issue | 2 |
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| container_title | Journal of radioanalytical and nuclear chemistry |
| container_volume | 283 |
| creator | Doostmohammadi, V. Sardari, D. Nasrabadi, A. M. |
| description | Prompt gamma spectrum produced by thermal neutron absorption and fast neutron inelastic scattering is simulated using Monte Carlo code MCNP4C. The simulated spectrum is analyzed with artificial neural network techniques. The neural network in our study is trained based on back-propagation algorithm with 138 gamma ray spectra. Elements existing in the 20 different substances are specified. The ANN could identify elements correctly in 96% of input cases. |
| doi_str_mv | 10.1007/s10967-009-0413-z |
| format | Article |
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The ANN could identify elements correctly in 96% of input cases.</description><subject>Algorithms</subject><subject>Chemistry</subject><subject>Chemistry and Materials Science</subject><subject>Computer simulation</subject><subject>Diagnostic Radiology</subject><subject>Fast neutrons</subject><subject>Gamma ray spectra</subject><subject>Gamma rays</subject><subject>Hadrons</subject><subject>Heavy Ions</subject><subject>Inelastic scattering</subject><subject>Inorganic Chemistry</subject><subject>Learning theory</subject><subject>Methods</subject><subject>Monte Carlo method</subject><subject>Monte Carlo methods</subject><subject>Neural networks</subject><subject>Nuclear Chemistry</subject><subject>Nuclear Physics</subject><subject>Physical Chemistry</subject><subject>Technology application</subject><issn>0236-5731</issn><issn>1588-2780</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2010</creationdate><recordtype>article</recordtype><recordid>eNp9kT1v1TAUhi0EEpfCD2DzBktafyS2M1ZXUJBascBsnSQnFxfHTm0H1C78dXwV5srDkezneW35JeQ9Z5ecMX2VOeuVbhjrG9Zy2Ty9IAfeGdMIbdhLcmBCqqbTkr8mb3K-ZxU0Rh7I32NcBhdworCu3o1QXAw0zvQuhoL0CMlHumD5GSsRJhpwS-DrKH9i-pVpiTS7ZfNQ4YcNvCs14TfSNcVlLfQEywJnqaQaC2M922-AAP4xu_yWvJrBZ3z3f16QH58_fT9-aW6_3Xw9Xt82o-xFaVrJe5RaD0pMYjAauVEK9YSmMz12rOPTpJiCdlYou5G1XSuEERNqmAwMg7wgH_bc-rCHDXOxi8sjeg8B45Ztz3QvW6Z1JT8-S3KtBBcdM6yilzt6Ao_WhTmWBGNdEy5ujAFnV_evpZLKaNaaKvBdGFPMOeFs1-QWSI-WM3vu0e492lqPPfdon6ojdidXNpww2fu4pfp9-RnpHyjYouw</recordid><startdate>20100201</startdate><enddate>20100201</enddate><creator>Doostmohammadi, V.</creator><creator>Sardari, D.</creator><creator>Nasrabadi, A. 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| ispartof | Journal of radioanalytical and nuclear chemistry, 2010-02, Vol.283 (2), p.403-407 |
| issn | 0236-5731 1588-2780 |
| language | eng |
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| source | Springer Nature - Complete Springer Journals |
| subjects | Algorithms Chemistry Chemistry and Materials Science Computer simulation Diagnostic Radiology Fast neutrons Gamma ray spectra Gamma rays Hadrons Heavy Ions Inelastic scattering Inorganic Chemistry Learning theory Methods Monte Carlo method Monte Carlo methods Neural networks Nuclear Chemistry Nuclear Physics Physical Chemistry Technology application |
| title | Combined application of Monte Carlo method and neural networks to simulate qualitative prompt gamma neutron activation analysis |
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