²⁵²Cf-source driven identification method for mass of fissile material based on autocorrelation function and stationary wavelet transform

According to the relationship between the autocorrelation function of neutron pulse signal and the mass of fissile material (252U), this paper proposes an identification method for the mass of fissile material by means of artificial neural network and stationary wavelet transform. In order to suppress the “noise effect” of autocorrelation function due to statistical fluctuation of neutron signal, the wavelet approximation subband of the 2nd level is extracted after the autocorrelation function is decomposed, and the subband coefficients of different mass are reused as the input variables of distributed Elman neural network for training and recognizing. The impact of the number of subnetworks is also studied. The experimental results show that, under an ideal condition (4 kinds of mass a