Parallel and optimized genetic Elman network for ²⁵²Cf source-driven verification system postprint

Abstract: The ²⁵²Cf source-driven verification system (SDVS) can recognize the enrichment of fissile material with the enrichment-sensitive autocorrelation functions of a detector signal in ²⁵²Cf source-driven noise-analysis (SDNA) measurements. We propose a parallel and optimized genetic Elman network (POGEN) to identify the enrichment of ²³⁵U based on the physical properties of the measured autocorrelation functions. Theoretical analysis and experimental results indicate that, for 4 different enrichment fissile materials, due to higher information utilization, more efficient network architecture, and optimized parameters, the POGEN-based algorithm can obtain identification results with higher recognition accuracy, compared to the integrated autocorrelation function (IAF) method.