基于可能性矩的混合不确定性全局灵敏度分析

doi:10.13700/j.bh.1001-5965.2016.0626

北京航空航天大学学报 ›› 2017, Vol. 43 ›› Issue (8): 1705-1712.doi: 10.13700/j.bh.1001-5965.2016.0626

• 论文 • 上一篇

基于可能性矩的混合不确定性全局灵敏度分析

成凯, 吕震宙, 石岩

西北工业大学航空学院, 西安 710072

收稿日期:2016-07-27 修回日期:2016-09-02 出版日期:2017-08-20 发布日期:2016-12-13
通讯作者: 吕震宙 E-mail:zhenzhoulu@nwpu.edu.cn
作者简介:成凯,男,硕士研究生。主要研究方向:可靠性工程、灵敏度分析;吕震宙,女,博士,教授,博士生导师。主要研究方向:飞行器设计及可靠性工程。
基金资助:
中央高校基本科研业务费专项资金（3102015BJ（Ⅱ） CG009）

Global sensitivity analysis under mixed uncertainty based on possibilistic moments

CHENG Kai, LYU Zhenzhou, SHI Yan

School of Aeronautics, Northwestern Polytechnical University, Xi'an 710072, China

Received:2016-07-27 Revised:2016-09-02 Online:2017-08-20 Published:2016-12-13
Supported by:
the Fundamental Research Funds for the Central Universities (3102015BJ (Ⅱ) CG009)

摘要/Abstract

摘要： 在同时包含随机不确定性和模糊不确定性结构系统中，为了分别度量随机输入变量和模糊输入变量对输出响应的统计特征的影响，提出了随机输入变量和模糊输入变量的全局灵敏度新指标。在模糊变量可能性矩定义的基础上，分析了混合不确定性下输出响应的特征。从输出响应可能性矩的角度出发，以输出响应的可能性期望为例，通过比较输出响应有条件和无条件可能性期望的概率密度函数（PDF）的平均差异，分别建立了随机输入变量和模糊输入变量关于输出响应的可能性期望的灵敏度指标。讨论了所提指标的性质，并采用Kriging代理模型来提高混合不确定性全局灵敏度指标的计算效率。最后通过算例验证了本文所提方法的准确性和高效性。

关键词: 模糊变量, 随机变量, 灵敏度分析, 可能性矩, Kriging代理模型

Abstract: For the structures with fuzzy uncertainty and random uncertainty simultaneously, to measure the influence of fuzzy and random input variables on the statistical characteristic of output response, a new global sensitivity index is proposed. Based on the definition of possibilistic moments of the fuzzy variable, the characteristic of the output response under mixed uncertainty is analyzed. With respect to the possibilistic moments of the output response, the possibilistic expectation of output response is taken as an example, and the average difference between the unconditional probability density function (PDF) and the conditional PDF of the model output possibilistic expectation is used to establish the global sensitivity indices for both the fuzzy input and the random input. The properties of the proposed global sensitivity indices are discussed, and the Kriging surrogate model is applied to solving the proposed index efficiently. Finally, some examples are used to verify the rationality and effectiveness of the proposed method.

Key words: fuzzy variable, random variable, sensitivity analysis, possibilistic moments, Kriging surrogate model

中图分类号:

TB114

成凯, 吕震宙, 石岩. 基于可能性矩的混合不确定性全局灵敏度分析[J]. 北京航空航天大学学报, 2017, 43(8): 1705-1712.

CHENG Kai, LYU Zhenzhou, SHI Yan. Global sensitivity analysis under mixed uncertainty based on possibilistic moments[J]. JOURNAL OF BEIJING UNIVERSITY OF AERONAUTICS AND A, 2017, 43(8): 1705-1712.

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基于可能性矩的混合不确定性全局灵敏度分析

Global sensitivity analysis under mixed uncertainty based on possibilistic moments

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