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Analysis and estimation of fading time from thermoluminescence glow curve by using artificial neural network

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dc.contributor.author Isik, E.
dc.contributor.author Isik, I.
dc.contributor.author Toktamis, H.
dc.date.accessioned 2022-10-06T12:50:22Z
dc.date.available 2022-10-06T12:50:22Z
dc.date.issued 2021
dc.identifier.issn 10420150 (ISSN)
dc.identifier.uri http://hdl.handle.net/11616/71780
dc.description.abstract The artificial neural network (ANN) is an information processing technology inspired by the information processing technique of the human brain. The way the simple biological nervous system works is imitated with ANN. In this study, an ANN model is proposed to analyze and simulate TL intensity of experimental data of quartz crystals with respect to the fading. In this model, network type and transfer function are chosen as the feed-forward backpropagation algorithm and Tansig respectively for the training of the proposed ANN model. The optimization process is also chosen as Levenberg–Marquardt in this study. The performance criteria of the proposed method were evaluated according to the coefficient of determination (R 2) and mean-squared error (MSE) techniques. After simulation results are obtained, the TL glow curve of the prediction results of quartz crystal is obtained as a function of fading time irradiated with β-source at 70 Gy for stored in 64 h at room temperature. © 2021 Informa UK Limited, trading as Taylor & Francis Group.
dc.source Radiation Effects and Defects in Solids
dc.title Analysis and estimation of fading time from thermoluminescence glow curve by using artificial neural network


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