A Comparison of the Use of Artificial Intelligence Methods in the Estimation of Thermoluminescence Glow Curves

Abstract

In this study, the thermoluminescence (TL) glow curve test results performed with eleven different dose values were used as training data, and its attempted to estimate the test results of the curves performed at four different doses using artificial intelligence methods. While the dose values of the data used for training were 10, 20, 50, 100, 150, 220, 400, 500, 600, 700, and 900 Gy, the selected dose values of the data for the testing were 40, 276, 320, and 800 Gy. The success of the experimental and artificial neural network results was determined according to the mean squared error (RMSE), regression error (R2), root squared error (RSE), and mean absolute error (MAE) criteria. Studies have been carried out on seven different neural network types. These networks are adaptive network-based fuzzy inference system (ANFIS), general regression neural network (GRNN), radial basis neural network (RBNN), cascade-forward backprop neural network (CFBNN), Elman backprop neural network (EBNN), feed-forward backprop neural network (FFBNN), and layer recurrent neural network (LRNN). This study concluded that the neural network with the Elman backpropagation network type demonstrated the best network performance. In this network, the training success rate is 80.8%, while the testing success rate is 87.95%.

Keywords

• thermoluminescence glow curve
• dose–response
• artificial intelligence
• neural network