To improve the reliability calculation efficiency of the Information Entropy Function(H)based on the Kriging model,the Weight Information Entropy Function(WH)is proposed.The WH learning function considers both locations of the sample points in the variable space and the probability density function of random variables.Different weights are given to the information entropy of the sample points.Sample points which approach not only to the limit state surface but also have large probability density function value,are selected to update the Kriging model.Consequently,it can reduce the number of calls to the performance func-tion and improve the efficiency of the reliability computation.Specifically,the proposed method is verified by several examples.The results show that this method requires fewer sample points in the process of establishing the Kriging model.Finally,compared with other active learning functions,it has fast convergence speed and high calculation efficiency.
关键词
结构可靠性分析/Kriging模型/学习函数/权重信息熵函数/概率密度函数
Key words
Structural Reliability Analysis/Kriging Model/Learning Function/Weight Information Entropy Func-tion/Probability Density Function
维普
万方数据