1.Key Laboratory of Opto-electronics Technology & System, Ministry of Education, Chongqing University, Chongqing 400044, China
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Peng FENG, Biao WEI, Jing JIN. Parallel and optimized genetic Elman network for 252Cf source-driven verification system. [J]. Nuclear Science and Techniques 26(4):040404(2015)
Peng FENG, Biao WEI, Jing JIN. Parallel and optimized genetic Elman network for 252Cf source-driven verification system. [J]. Nuclear Science and Techniques 26(4):040404(2015) DOI： 10.13538/j.1001-8042/nst.26.040404.
The ,252,Cf source-driven verification system (SDVS) can recognize the enrichment of fissile material with the enrichment-sensitive autocorrelation functions of a detector signal in ,252,Cf source-driven noise-analysis (SDNA) measurements. We propose a parallel and optimized genetic Elman network (POGEN) to identify the enrichment of ,235,U 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.
Nuclear noise analysisNeutron detectionParallel and optimized genetic Elman networkEnrichment identification
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