Using artificial neural networks for an estimation of the nuclear reaction cross section data is discussed. Approximately rate of the fitting criteria is determined by the calculated experimental data obtained from using Variable Learning Rate Backpropagation (traingdx) algorithm in artificial neural networks. This method has been applied to obtain the cross section for 14–15 MeV neutron-induced (n,α) and (n,p) reactions in the fusion reactor structural materials. In comparison to the reaction cross section calculation by experimental cross sections reported in EXFOR, TALYS 1.9 and EMPIRE 3.2, the proposed method has better prediction ability when the target output has a large variation between the experimental and the calculated data. This study is substantial for the new method validation development of the nuclear model approaches with the increased prediction power of the neutron-induced reactions for fusion reactor systems.

讨论了使用人工神经网络估计核反应截面数据。拟合标准的近似速率由在人工神经网络中使用可变学习速率反向传播（traingdx）算法获得的计算实验数据确定。该方法已用于获得14-15 MeV中子感应的（n，α）和（n，p）反应在聚变反应堆结构材料中的作用。与EXFOR，TALYS 1.9和EMPIRE 3.2中报告的通过实验截面计算反应截面相比，当目标输出在实验数据和计算数据之间存在较大差异时，该方法具有更好的预测能力。这项研究对于核模型方法的新方法验证开发具有实质性意义，随着核反应堆系统中子诱发反应的预测能力的增强，该研究已得到广泛应用。