期刊
RADIATION PHYSICS AND CHEMISTRY
卷 86, 期 -, 页码 10-22出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.radphyschem.2013.01.021
关键词
Buildup factor; Gamma-ray; Energy absorption; Thermo luminescence dosimetry; Neural network
资金
- Commission of Scientific Research Projects of Uludag University [UAP(F)-2011/74]
In this work, multilayered perceptron neural networks (MLPNNs) were presented for the computation of the gamma-ray energy absorption buildup factors (BA) of seven thermoluminescent dosimetric (TLD) materials [LiF, BeO, Na2B4O7, CaSO4, Li2B4O7, KMgF3, Ca-3(PO4)(2)] in the energy region 0.015-15 MeV, and for penetration depths up to 10 mfp (mean-free-path). The MLPNNs have been trained by a Levenberg-Marquardt learning algorithm. The developed model is in 99% agreement with the ANSI/ANS-6.43 standard data set. Furthermore, the model is fast and does not require tremendous computational efforts. The estimated BA data for TLD materials have been given with penetration depth and incident photon energy as comparative to the results of the interpolation method using the Geometrical Progression (G-P) fitting formula. (C) 2013 Elsevier Ltd. All rights reserved.
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