Development of a modular system for estimating attenuation coefficient of gamma radiation: comparative study of different learning algorithms of cascade feed-forward neural network

In this paper, a modular system is developed for estimation of mass attenuation coefficient (MAC) of different materials/energies using artificial neural network (ANN). Cascade feed-forward neural network (CFFNN) as a type of ANN constructs mapping function between input patterns and the targets (i.e. MAC). Performance of different learning algorithms of CFFNN including gradient descent (GD), gradient descent with momentum (GDM), scaled conjugate gradient (SCG), Levenberg-Marquardt (LM), and Bayesian regularization (BR) are compared. For training, different categories of input patterns features are utilized to show the more appropriate one. Average mean relative error (AMRE) and cumulative distribution function (CDF) of the results indicate that BR learning algorithm accompany with the selected category of features (i.e. Z, E, and ρ) is more accurate for estimation of MAC (e.g. CDF_Al (0.0069) = 0.99 and AMRE_Al = 0.0017). The advantages of the present method are: 1- Estimation of MAC is done fast (i.e. in comparison with Monte Carlo methods) and is done at a lower cost (i.e. without need to extra experiments) 2- Modular system reduces the risk of incorrect estimation 3- It is possible to extend the number of estimators for more materials/mixtures without unfavorably affecting the existing system.