Use of Neural Networks for Monitoring Beam Spectrum of Industrial Electron Accelerators

This paper investigates technique for solving spectrometry inverse problem the neural network as method for reconstruction of electron beam spectrum using depth-charge curve. The inverse problem turned into multivariable optimization and the form of spectrum is based on proposed three-parameter model. Radial basis function network calculates the parameters of this model. We developed computational experiment using Monte-Carlo technique to evaluate strengths and weaknesses of proposed approach and compare neural networks with conventional data evaluation methods.