Artificial neural networks for neutron source localization within sealed tanks

A modular back-propagation ANN has been implemented for the non-destructive localization of a source of Even Plutonium Isotopes (EPI) contained in sealed tanks. The ANN has been trained on data obtained from a simulation of a well counter (filtered and Fourier transformed signals of the neutron detectors surrounding the well counter) for known positions of the EPI. After training, the ANN can predict the position of EPI within sealed tanks from the corresponding detector signals. The introduction of median and majority ANNs has been found to significantly improve the accuracy of prediction. Furthermore, these ANNs perform in a satisfactory manner when noise is injected to the detector signals; prediction is corrupted in a manner which is directly related to the extent and amount of noise.

The motivation for using back-propagation ANNs is twofold: on one hand (theoretical importance), they are capable of learning to approximate complex functions such as the strongly non-linear relation that exists between the neutron detector signals and the EPI position; on the other hand, they accomplish on-line localization which is of practical interest.