Affiliation of Author(s):航空学院
Journal:Xibei Gongye Daxue Xuebao
Abstract:The contribution of input random variables to failure probability of an uncertain system can be characterized by failure probability based global importance indices. In this paper, cross entropy method (CEM) is introduced to calculate global importance indices to address the issue of constructing an appropriate importance sampling probability density function (PDF) in the traditional important sampling method. In essential, CEM is an adaptive important sampling method which selects a proper important sampling PDF in a progressive way to avoid the calculation of position and number of design points. Comparing the associate theories and computational results, it can be seen that the traditional importance sampling is applicable to simple cases where design points can be easily found only while CEM possesses high computational efficiency and accuracy for problems with multiple design points and complex limit state function. © 2017, Editorial Board of Journal of Northwestern Polytechnical University. All right reserved.
ISSN No.:1000-2758
Translation or Not:no
Date of Publication:2017-06-01
Co-author:Ren, Chao
Correspondence Author:lhs
Date of Publication:2017-06-01