Optimization of a neural network model for signal-to-background prediction in gamma-ray spectrometry

Abstract

The artificial neural network (ANN) model was optimized for the prediction of signal-tobackground (SBR) ratio as a function of the measurement time in gamma-ray spectrometry. The network parameters: learning rate (α), momentum (µ), number of epochs (E) and number of nodes in hidden layer (N) were optimized simultaneously employing variable-size simplex method. The most accurate model with the root mean square (RMS) error of 0.073 was obtained using ANN with online backpropagation randomized (OBPR) algorithm with α = 0.27, µ = 0.36, E = 14800 and N = 9. Most of the predicted and experimental SBR values for the eight radionuclides (226Ra, 214Bi, 235U, 40K, 232Th, 134Cs, 137Cs and 7 Be), studied in this work, reasonably agreed to within 15 %, which was satisfactory accurace.