Abstract
This work presents a new approach based on multilayered perceptrons (MLPs) to compute the thermoluminescence glow curves in undoped and La-doped zinc borate crystals. The MLP has been trained by a genetic algorithm (GA) and a Levenberg-Marquardt learning algorithm. The results obtained using the MLP models were tested with untrained experimental glow curve data. The comparison has shown that the proposed model can predict more accurately and easily the thermoluminescence glow curves.
Highlights
Controlled measurement of the emitted light of a dosimetric material is normally used for determination of the radiation dose absorbed by that material [ ]
The developed TL model is very useful for getting detailed information on kinetic parameters of trapping centers in the dosimetric material
E and s have been determined by variable heating rate (VHR), initial rise (IR), isothermal decay (ID), and peak shape (PS) methods
Summary
Controlled measurement of the emitted light (glow curve) of a dosimetric material is normally used for determination of the radiation dose absorbed by that material [ ]. The purpose of the present work is to model the thermoluminescence glow curves of trapping centers in undoped and La-doped zinc borate crystals using GA, ANNs, and experimental data. They have been adapted to the computation of the thermoluminescence glow curves of trapping centers in undoped and La-doped zinc borate crystals.
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