In this study; Giant Dipole Resonance (GDR) parameters of the spherical nucleus have been estimated by using artificial neural network (ANN) algorithms. The ANN training has been carried out with the Levenberg–Marquardt feed-forward algorithm in order to provide fast convergence and stability in ANN training and experimental data, taken from Reference Input Parameter Library (RIPL). R values of the system have been found as 0.99636, 0.94649, and 0.98318 for resonance energy, full width half maximum, and resonance cross-section, respectively. Obtained results have been compared with the GDR parameters which are taken from the literature. To validate our findings, newly acquired GDR parameters were then replaced with the existing GDR parameters in the TALYS 1.95 code and ^142-146Nd($\gamma ,n)$^141-145Nd reaction cross-sections have been calculated and compared with the experimental data taken from the literature. As a result of the study, it has been shown that ANN algorithms can be used to calculate the GDR parameters in the absence of the experimental data.

在这个研究中; 球形核的巨偶极共振（GDR）参数已通过使用人工神经网络（ANN）算法进行了估算。为了使用来自参考输入参数库（RIPL）的ANN训练和实验数据提供快速的收敛性和稳定性，已经使用Levenberg-Marquardt前馈算法进行了ANN训练。对于共振能量，半峰全宽和共振截面，该系统的R值分别为0.99636、0.94649和0.98318。将获得的结果与文献中的GDR参数进行了比较。为了验证我们的发现，然后将新获得的GDR参数替换为TALYS 1.95代码和^142-146 Nd（$\gamma ，ñ）$已计算出^141-145 Nd反应截面，并将其与从文献中获得的实验数据进行比较。研究的结果表明，在没有实验数据的情况下，可以使用ANN算法来计算GDR参数。