Artificial Neural Network for Spectrum unfolding Bonner Sphere Data
Date of Award
Master of Science
Laurence F. Miller
Ronald E. Pevey, J. Wesley Hines
The use of Bonner Sphere Spectrometer (BSS) is a well-established method of measuring the energy distribution of neutron emission sources. The purpose of this research is to apply the Generalized Regression Neural Network (GRNN), a kind of Artificial Neural Network (ANN), to predict the neutron spectrum using the count rate data from a BSS. The BSS system was simulated with the MCNP5 Monte-Carlo code to calculate the response to neutrons of different energies for each combination of thermal neutron detector and polyethylene sphere. One hundred and sixty-three different types of neutron spectra were then investigated. GRNN Training and testing was carried out in the MATLAB environment. In the GRNN testing, eight-one predicted spectra were obtained as outputs of the GRNN. Comparison with standard spectra shows that 97.5% of the prediction errors were controlled below 1%, indicating ANN could be used as an alternative with high accuracy in neutron spectrum unfolding methodologies. Advantages and further improvements of this technique are also discussed.
Hou, Jia, "Artificial Neural Network for Spectrum unfolding Bonner Sphere Data. " Master's Thesis, University of Tennessee, 2007.