基于人工神经网络的核素识别分析方法

Nuclide Identification and Analysis Using Artificial Neural Network

针对传统的方法解析γ谱数据实现核素识别存在步骤多、精度低、专业知识要求高、识别速度慢等缺点,提出了一种基于人工神经网络的核素识别分析方法。在对全谱γ数据进行主成分分析的基础上,提取出γ谱的主要特征,将此特征信息输入人工神经网络,利用BP网络算法和RBF网络算法可快速地完成γ谱解析。分析结果表明:该方法降低了对探测器能量分辨率的要求,同时避免了寻峰、能量刻度与效率刻度等问题,简化了核素识别的过程,有效地提高了放射性核素的快速识别能力。

The traditional method of nuclide identification by analyzing the data inγ spectrum takes various steps and suffers low accuracy and identification speed. In addition, it requires much more professional knowledge and work. This paper proposed a novel method of nuclide identification and analysis using neural network. Based on the principal component analysis （PCA） of all the data inγ spectrum, it extracts the main features ofγ spectrum and then imports the extracted information into the neural network. It can implement fast analysis of the data inγ spectrum with high accuracy by using BP and RBF network algorithms. The experimental results show that our proposed method does not require high energy resolution of detector as well as solves the problems of peak searching, energy calibration and efficiency calibration. Therefore, it simplifies the process of nuclide identification and significantly improves the ability of fast identification of radioactive nuclide.