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Prediction of peak-to-background ratio in gamma-ray spectrometry using simplex optimized artificial neural network
dc.creator | Dragović, Snežana D. | |
dc.creator | Onjia, Antonije | |
dc.date.accessioned | 2021-03-10T10:28:04Z | |
dc.date.available | 2021-03-10T10:28:04Z | |
dc.date.issued | 2005 | |
dc.identifier.issn | 0969-8043 | |
dc.identifier.uri | http://TechnoRep.tmf.bg.ac.rs/handle/123456789/853 | |
dc.description.abstract | An artificial neural network (ANN) model was used for the prediction of peak-to-background ratio (PBR) as a function of measurement time in gamma-ray spectrometry. In order to make the ANN model with good predictive power, the ANN parameters were optimized simultaneously employing a variable-size simplex method. Most of the predicted and the experimental PBR values for eight radionuclides (Ra-226, U-238, U-235, K-40, Th-232, Cs-134, Cs-137, and Be-7) commonly detected in soil samples agreed to within +/- 19.4% of the expanded uncertainty and 2.61% of average bias. | en |
dc.publisher | Pergamon-Elsevier Science Ltd, Oxford | |
dc.rights | restrictedAccess | |
dc.source | Applied Radiation and Isotopes | |
dc.subject | ANN | en |
dc.subject | radionuclides | en |
dc.subject | PBR | en |
dc.subject | soil | en |
dc.subject | measurement time | en |
dc.title | Prediction of peak-to-background ratio in gamma-ray spectrometry using simplex optimized artificial neural network | en |
dc.type | article | |
dc.rights.license | ARR | |
dc.citation.epage | 366 | |
dc.citation.issue | 3 | |
dc.citation.other | 63(3): 363-366 | |
dc.citation.rank | M22 | |
dc.citation.spage | 363 | |
dc.citation.volume | 63 | |
dc.identifier.doi | 10.1016/j.apradiso.2005.03.009 | |
dc.identifier.pmid | 15927476 | |
dc.identifier.scopus | 2-s2.0-21844453922 | |
dc.identifier.wos | 000231043100010 | |
dc.type.version | publishedVersion |