• 辐射传输与屏蔽 •

### BP神经网络算法预测多组分材料中子屏蔽效果方法研究

1. 中国辐射防护研究院,太原 030006
• 出版日期:2020-11-20 发布日期:2021-01-27
• 作者简介:林海鹏(1985—),男,2006年本科毕业于华北水利水电大学环境工程专业,2009年硕士毕业于中国环境科学研究院环境工程专业,副研究员。E-mail:linhpcirp@126.com

### Study on BP neural network algorithm for predicting neutron shieldingeffect of multi-composition materials

LIN Haipeng, LI Guodong, CHEN Faguo, HAN Yi, LIANG Runcheng

1. China Institute for Radiation Protection,Taiyuan 030006
• Online:2020-11-20 Published:2021-01-27

Abstract: The availability of applying BP neural network algorithm to boost the optimization process of multi-composition neutron shielding materials,in which conventional Monte Carlo simulation would cause severe time consumption,has been discussed.A typical 3-layer BP neural network model has been established with 300 random mass components of composite materials and their corresponding dose values calculated by Monte Carlo as training samples.The absolute deviations between the predicted dose value and the sample value are within ±2.For validation samples outside the training samples,the absolute deviations expend to -6.4 to 5.2.According to the deviation distribution statistics,for over 70% of the total samples,the relative deviation absolute values are within 2%.The calculation accuracy and generalization ability of the neural network model are qualitatively determined to meet the requirements of the optimization algorithm.The cross-validation method can improve the calculation accuracy of the training samples,whereas increasing the calculation deviation of the verification samples,indicating that the balance between the fitting degree and generalization ability of the samples should be considered during the establishment of the neural network.

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