基于神经网络算法的组件截面表达方式研究
Cross Section Expression Based on Neural Network Algorithm
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摘要: 为通过事先算好的组件均匀化截面产生堆芯计算所需的随组件当地工况变化的截面,本文采用了神经网络算法对样本截面进行函数逼近。选取贝叶斯规则和提前终止相结合的方法对BP网络进行训练,利用两个算例对其进行检验。并通过与FITLINK计算结果的对比表明,基于神经网络算法的截面表达方式可行。Abstract: n order to obtain the cross sections in different operating conditions affected by the thermal parameters and depletion, the neural network algorithm was used for function approximation. Bayesian normalization function and the early termination method were used to train sample cross section. Further more, this method was tested by the two examples. By comparing with FITLINK, the results show that cross section expression based on neural network algorithm is feasible.