基于BP神经网络的核事故多核素源项反演方法

核事故发生后，为快速评估事故严重程度，需要对源项释放率进行估算。本文选取I-131，Cs-137，Xe-133和Kr-85四种核素的释放率为目标信号，利用Matlab建立基于BP神经网络的核事故四核素源项反演模型。计算结果表明，在单隐层节点数为5~60范围内，训练均方差 随节点数增加先减小后增大，在节点数为25时达到最小值41.1%。学习速率在0.01~0.2范围内时，增大学习率可减小训练均方差与各核素相对误差。对单隐层节点数为25，学习速率为0.2的训练结果进行测试，4种核素的源项估计相对误差分别为56.7%，49.1%，92.4%和92.0%。

Multi-nuclide Source Term Inversion Based on BP Neural Network During Nuclear Accident

To estimate the severity of nuclear accident, a back propagation (BP) neural network basic model is built for source term inversion during a nuclear accident. The release rates of I-131, Cs-137, Xe-133 and Kr-85 are selected as target signals, and the Matlab software is used to perform the calculations for source term inversion. The results show that in a single hidden layer, the train mean square error decreases firstly but increases thereafter with increasing the number of nodes from 5 to 60, and reaches the minimum value of 41.1% when the number of nodes is 25. Increasing the learning rate from 0.01 to 0.2 can reduce the relative error variance for each nuclide. The relative errors of release rates of I-131, Cs-137, Xe-133 and Kr-85 are 6.7%, 49.7%, 92.3% and 92.0%, respectively, when the learning rate is 0.2. The source term inversion is tested at the node number of 25 and the learning rate of 0.2, and the results show that the relative test errors of release rates of I-131, Cs-137, Xe-133 and Kr-85 are 56.7%, 49.1%, 92.4% and 92.0%, respectively.