基于MLP神经网络优化改进的BW模型  

Improved BW model based on MLP neural network optimization

作　　者：陈存宇 陈爱喜 戚晓秋 王韩奎 CHEN Cunyu;CHEN Aixi;QI Xiaoqiu;WANG Hankui(School of Science,Zhejiang Sci-Tech University,Hangzhou 310018,China)

机构地区：[1]浙江理工大学理学院,杭州310018

出　　处：《物理学报》2025年第1期114-124,共11页Acta Physica Sinica

基　　金：国家自然科学基金(批准号:U2267205,12475124,12175199,12204412);浙江理工大学人才启动基金(批准号:22062267-Y,21062349-Y)资助的课题.

摘　　要：神经网络具有强大的建模能力和对大规模数据的适应性,在拟合核质量模型参数方面表现出显著效果.本研究旨在探索神经网络拟合核质量模型参数的问题:采用多层感知机(multilayer perceptron,MLP)神经网络结构,评估不同参数下Adam优化器的训练效果,训练出准确的模型参数.研究发现,基于AME2020数据,更新系数后的BW2核质量模型在双幻数以及重核区域的均方根误差降低明显;BW3模型重新拟合后的全局均方根误差为1.63 MeV,较之前1.86 MeV有所降低.结果表明,该方法能够有效地拟合模型参数,并具有良好的拟合性能和泛化能力.这项研究为BW系列核质量模型的系数提供了新的拟合方法,也为其他核质量寻求最佳拟合参数提供了有益的参考.The nuclear mass model has significant applications in nuclear physics,astrophysics,and nuclear engineering.The accurate prediction of binding energy is crucial for studying nuclear structure,reactions,and decay.However,traditional mass models exhibit significant errors in double magic number region and heavy nuclear region.These models are difficult to effectively describe shell effect and parity effect in the nuclear structure,and also fail to capture the subtle differences observed in experimental results.This study demonstrates the powerful modeling capabilities of MLP neural networks,which optimize the parameters of the nuclear mass model,and reduce prediction errors in key regions and globally.In the neural network,neutron number,proton number,and binding energy are used as training feature values,and the mass-model coefficient is regarded as training label value.The training set is composed of the multiple sets of calculated nuclear mass model coefficients.Through extensive experiments,the optimal parameters are determined to ensure the convergence speed and stability of the model.The Adam optimizer is used to adjust the weight and bias of the network to reduce the mean squared error loss during training.Based on the AME2020 dataset,the trained neural network model with the minimum loss is used to predict the optimal coefficients of the nuclear mass model.The optimized BW2 model significantly reduces root-mean-square errors in double magic number and heavy nuclear regions.Specifically,the optimized model reduces the root-mean-square error by about 28%,12%,and 18%near Z=50 and N=50;Z(N)=50 and N=82;Z=82 and N=126,respectively.In the heavy nuclear region,the error is reduced by 48%.The BW3 model combines higher-order symmetry energy terms,and after parameter optimization using the neural network,reduces the global root-mean-square error from 1.86 MeV to 1.63 MeV.This work reveals that the model with newly optimized coefficients not only exhibit significant error reduction near double magic numbers,but also s

关 键 词：核质量模型 幻数 MLP神经网络 Adam优化器 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] O571[自动化与计算机技术—控制科学与工程]