Neural networks for predicting neutron ambiente dose equivalent measured by means of bonner spheres

Abstract

A Neural Network structure has been applied for predicting neutron Ambient Dose Equivalent measured by means of a Bonner Sphere Spectrometer (BSS) set. The present work used the SNNS (“Stuttgart Neural Network Simulator”) as the interface for designing, training and validation of a MultiLayer Perceptron network. The back-propagation algorithm was applied . The Bonner Sphere set chosen has been calibrated at the National Physical Laboratory, United Kingdom, and uses gold activation foils as thermal neutron detectors. The neutron energy covered by the response functions goes from 0.0001 eV to 10 MeV. A set of 27 continuous neutron spectra was used for training and validating the neural network. Excellent results were obtained, indicating that the Neural Network can be considered an interesting alternative for estimating neutron Ambient Dose Equivalent measured by means of Bonner Spheres.