典型文献
Prediction of nuclear charge density distribution with feedback neural network
文献摘要：
Nuclear charge density distribution plays an important role in both nuclear and atomic physics,for which the two-parameter Fermi(2pF)model has been widely applied as one of the most frequently used models.Currently,the feedforward neural network has been employed to study the available 2pF model parameters for 86 nuclei,and the accuracy and precision of the parameter-learning effect are improved by introducing A1/3 into the input parameter of the neural network.Furthermore,the average result of multiple predictions is more reliable than the best result of a single prediction and there is no significant difference between the average result of the density and parameter values for the average charge density distribution.In addi-tion,the 2pF parameters of 284(near)stable nuclei are predicted in this study,which provides a reference for the experiment.
文献关键词：
中图分类号：
作者姓名：
Tian-Shuai Shang;Jian Li;Zhong-Ming Niu
作者机构：
College of Physics,Jilin University,Changchun 130012,China;School of Physics and Optoelectronic Engineering,Anhui University,Hefei 230601,China
文献出处：
引用格式：
[1]Tian-Shuai Shang;Jian Li;Zhong-Ming Niu-.Prediction of nuclear charge density distribution with feedback neural network)[J].核技术（英文版）,2022(12):24-35
A类：
2pF
B类：
Prediction,nuclear,charge,density,distribution,feedback,neural,network,Nuclear,plays,important,role,both,atomic,physics,which,Fermi,has,been,widely,applied,one,most,frequently,used,models,Currently,feedforward,employed,study,available,parameters,nuclei,accuracy,precision,learning,effect,are,improved,by,introducing,A1,into,input,Furthermore,average,result,multiple,predictions,reliable,than,best,single,there,no,significant,difference,between,values,In,addi,near,stable,predicted,this,provides,reference,experiment
AB值：
0.539589
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