Nuclear Science and Techniques

《核技术》(英文版) ISSN 1001-8042 CN 31-1559/TL     2019 Impact factor 1.556

Nuclear Science and Techniques ›› 2015, Vol. 26 ›› Issue (4): 040404 doi: 10.13538/j.1001-8042/nst.26.040404

• NUCLEAR ELECTRONICS AND INSTRUMENTATION • Previous Articles     Next Articles

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

FENG Peng, WEI Biao,JIN Jing   

  1. Key Laboratory of Opto-electronics Technology & System, Ministry of Education, Chongqing University, Chongqing 400044, China
  • Contact: FENG Peng E-mail:coe-fp@cqu.edu.cn
  • Supported by:

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

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FENG Peng, WEI Biao, JIN Jing . Parallel and optimized genetic Elman network for 252Cf source-driven verification system.Nuclear Science and Techniques, 2015, 26(4): 040404     doi: 10.13538/j.1001-8042/nst.26.040404

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.

Key words: Nuclear noise analysis, Neutron detection, Parallel and optimized genetic Elman network, Enrichment identification