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

he 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.