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

作　　者：冯鹏 魏彪 金晶 

机构地区：[1]Key Laboratory of Opto-electronics Technology & System,Ministry of Education, Chongqing University

出　　处：《Nuclear Science and Techniques》2015年第4期65-71,共7页核技术（英文）

基　　金：Supported by National Natural Science Foundation of China(Nos.61201346,61175005 and 61401049);the Fundamental Research Funds for the Central Universities(No.CDJZR14125501)

摘　　要：The 252Cf source-driven verification system(SDVS)can recognize the enrichment of fissile material with the enrichment-sensitive autocorrelation functions of a detector signal in252Cf source-driven noise-analysis(SDNA)measurements.We propose a parallel and optimized genetic Elman network(POGEN)to identify the enrichment of235U 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.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.

关 键 词：ELMAN网络 并行优化 验证系统 源驱动 遗传 自相关函数 函数识别 信息利用率 

分 类 号：TL825[核科学技术—核技术及应用]