Neutron spectrum unfolding using three artificial intelligence optimization methods

Abstract

Many methods have been proposed and developed in research into neutron spectrum unfolding. In this work, three artificial intelligence optimization methods—genetic algorithms, radial basis function neural networks and generalized regression neural networks—were developed on the basis of former research to retrieve the neutron spectrum. Sixty-three neutron spectra were unfolded on the basis of the same response functions with the three methods, and three indexes—the mean squared error, the spectral quality and the sphere reading quality—were applied with the aim to compare the generalized unfolding performance. The results obtained with the three methods show that the unfolded neutron spectra are mostly acceptable using three methods without the initial guess spectra and that the generalized regression neural network method is the fastest and most accurate method with the most powerful generalization ability.