The aim of this study is to develop an accurate artificial neural network algorithm for the cross-section of (n,p) reactions at 14.5 $\mp 0.5$ MeV neutron energy which is important to developing materials for fusion reactor design. The experimental data used at artificial Neural network calculations have been taken from the Experimental Nuclear Reaction Data (EXFOR) database. Bayesian algorithm has been used at training section of artificial neural network. Regression (R) values of artificial neural network calculations have been found as 0.99363, 0.98574 and 0.99257 for training, testing and all process respectively. In addition to artificial neural network calculations, TALYS 1.95 nuclear reaction code has been used to reproduce (n,p) reactions at 14.5 $\mp 0.5$ MeV. Two-component exciton model and Constant Temperature Fermi Gas Model have been used as pre-equilibrium and level density models respectively. Mean square errors of our calculations have been found 48.51 and 495.06 for artificial neural network and TALYS 1.95 respectively. Artificial Neural network estimations have been compared and analyzed with the TALYS 1.95 calculations and the experimental data taken from EXFOR database.

这项研究的目的是为（n，p）反应在14.5处的横截面开发一种精确的人工神经网络算法 $\mp 0.5$MeV中子能量对于开发聚变反应堆设计材料非常重要。用于人工神经网络计算的实验数据已从实验核反应数据（EXFOR）数据库中获取。贝叶斯算法已被用于人工神经网络的训练部分。对于训练，测试和所有过程，人工神经网络计算的回归（R）值分别为0.99363、0.98574和0.99257。除了人工神经网络计算，还使用了TALYS 1.95核反应代码来重现14.5时的（n，p）反应 $\mp 0.5$MeV。两组分激子模型和恒温费米气体模型分别用作预平衡模型和能级密度模型。对于人工神经网络和TALYS 1.95，我们的计算的均方误差分别为48.51和495.06。人工神经网络估计已与TALYS 1.95计算和从EXFOR数据库获得的实验数据进行了比较和分析。