The main aim of this study is to develop accurate artificial neural network (ANN) algorithms to estimate level density parameters. An efficient Bayesian-based algorithm is presented for classification algorithms. Unknown model parameters are estimated using the observed data, from which the Bayesian-based algorithm is predicted. This paper focuses on the Bayesian method for parameter estimations of Gilbert Cameron Model (GCM), Back Shifted Fermi Gas Model (BSFGM) and Generalised Super Fluid Model (GSM), which are known as the phonemological level density models. Obtained level density parameters have been compared with the Reference Input Parameter Library for Calculation of Nuclear Reactions and Nuclear Data Evaluations (RIPL) data. R values of the Bayesian method have been found as 0.9946, 0.9981 and 0.9824 for BSFGM, GCM and GSM, respectively. In order to validate our results, default level density parameters of TALYS 1.95 code have been changed with our newly obtained results and photo-neutron cross-section calculations of the ¹¹⁷Sn($\gamma ,n)$¹¹⁶Sn, ¹¹⁸Sn($\gamma ,n)$¹¹⁷Sn, ¹¹⁹Sn($\gamma ,n)$¹¹⁸Sn and ¹²⁰Sn($\gamma ,n)$¹¹⁹Sn reactions have been calculated by using these newly obtained level density parameters.

这项研究的主要目的是开发精确的人工神经网络（ANN）算法来估计水平密度参数。提出了一种有效的基于贝叶斯算法的分类算法。使用观察到的数据估计未知的模型参数，从中预测出基于贝叶斯算法。本文重点介绍了吉尔伯特·卡梅隆模型（GCM），后移费米气体模型（BSFGM）和广义超流体模型（GSM）的参数估计的贝叶斯方法，这些方法被称为音素级密度模型。已将获得的能级密度参数与参考输入参数库进行了比较，以用于计算核反应和核数据评估（RIPL）数据。对于BSFGM，GCM和GSM，贝叶斯方法的R值分别为0.9946、0.9981和0.9824。¹¹⁷锡（$\gamma ，ñ）$¹¹⁶锡，¹¹⁸锡（$\gamma ，ñ）$¹¹⁷锡，¹¹⁹锡（$\gamma ，ñ）$¹¹⁸锡和¹²⁰锡（$\gamma ，ñ）$通过使用这些新获得的能级密度参数，已经计算出^119个Sn反应。