Yayın: Modeling of gamma ray energy-absorption buildup factors for thermoluminescent dosimetric materials using multilayer perceptron neural network: A comparative study
Tarih
Kurum Yazarları
Küçük, Nil
Yazarlar
Manohara, S. R.
Hanagodimath, S. M.
Gerward, Leif
Danışman
Dil
Türü
Yayıncı:
Pergamon-Elsevier Science
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Özet
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.
Açıklama
Kaynak:
Anahtar Kelimeler:
Konusu
Chemistry, Nuclear science & technology, Physics, Buildup factor, Gamma-ray, Energy absorption, Thermo luminescence dosimetry, Neural network, Geometrical progression, Training algorithms, 100 mfp, Approximation, Technologies, Prediction, Parameters, Signals, Depths, Dosimetry, Energy absorption, Neural networks, Thermoluminescence, Buildup factor, Computational effort, Incident photon energy, Interpolation method, Levenberg-Marquardt learning algorithms, Multi-layer perceptron neural networks, Multi-layered Perceptron, Thermoluminescence dosimetry, Gamma rays
Alıntı
Küçük, N. vd. (2013). "Modeling of gamma ray energy-absorption buildup factors for thermoluminescent dosimetric materials using multilayer perceptron neural network: A comparative study". Radiation Physics and Chemistry, 85, 10-22.