计算力学学报2017，Vol.34Issue(4)：411-416,6.DOI:10.7511/jslx201704002
基于主成分分析的结构不确定性建模与传播研究
Structural uncertainty modeling and propagation based on principal component analysis
摘要
Abstract
This paper proposes a new structural uncertainty modeling method based on principal component analysis.First,the sample data of uncertain structure parameters are analyzed through principal component analysis method,and the corresponding orthogonal eigenvectors can be obtained.Then the sample data are projected to the new coordinate system which are established based on the eigenvector direction.Finally,the boundaries of uncertain parameters on the new coordinate system are calculated so that the non-probabilistic interval model for modeling the uncertainties of structure parameters is established.The uncertainty model based on principal component analysis is relatively compact,and it can transform the correlated parameters to uncorrelated parameters while the uncertainty model is established,which is convenient to efficiently solve uncertainty propagation problems.Two examples of uncertainty propagation that compared with the traditional interval model and parallelepiped model demonstrate the correctness and effectiveness of the proposed method.关键词
不确定性建模/主成分分析/非概率凸模型/不确定性传播/区间模型/相关性Key words
uncertainty modeling/principal component analysis/non-probabilistic convex model/uncertainty propagation/interval model/correlation分类
数理科学引用本文复制引用
刘杰,谢凌,卿宏军,刘浩..基于主成分分析的结构不确定性建模与传播研究[J].计算力学学报,2017,34(4):411-416,6.基金项目
国家自然科学基金(11572115) （11572115）
中央高校基本科研业务费 （）
湖南大学汽车车身先进设计制造国家重点实验自主研究课题(51475003)资助项目. （51475003）
