BP神经网络反演核事故源项中重要参数的研究

Important Parameters in Inversion of Nuclear Accident Source Term Based on BP Neural Network

核事故发生时,可靠、准确的源项信息能为应急防护行动措施决策提供数据支持。采用Matlab软件神经网络工具箱可以实现基于BP神经网络的核事故源项反演,为提高核事故源项反演计算的准确度,针对反演时的几个重要参数进行研究,包括隐含层节点数、训练函数、学习率和隐含层数。研究结果表明,在单隐含层神经网络结构情况下,存在着最优隐含层节点数,综合考虑训练时间和误差,本文选取隐含层节点数为50来对其他参数影响进行进一步研究;在相同参数设置条件下,训练函数Trainlm比Traingdm更适合数据量较小时的核事故源项反演,反演计算准确度更高,在节点数为50时训练时间缩短了近35%;高学习率以及双隐含层能有效地提高核事故源项反演的精度,但训练时间相对增加。

When the nuclear accident occurs,the reliable source terms can provide the data support for emergency response measures effectively.The network toolbox of Matlab can be used to realize the nuclear accident source term inversion based on the BP neural network.To improve the accuracy of nuclear accident source term inversion calculation,several important parameters in nuclear accident source term inversion are studied based on the BP neural network,including the number of hidden layer nodes,the kind of training function,the learning rate,and the number of hidden layers.The results show that the optimal hidden layer node number can be figured out in the single hidden layer,and based on the training time and error,the hidden layer node number selected for further studies is 50.Under the condition of the same parameter settings,the training function″Trainlm″is more suitable than″Traingdm″when the amount of nuclear accident source term data is small.And the inversion calculation accuracy by″Trainlm″is higher and the training time is shortened by nearly 35% when the hidden layer node number is 50.The high learning rate and double hidden layers can effectively improve the accuracy of the nuclear accident source term inversion,but the training time relatively increases.