基于随机集理论的相关变量模型不确定性量化

doi:10.16182/j.issn1004731x.joss.201706016

系统仿真学报 ›› 2017, Vol. 29 ›› Issue (6): 1277-1283.doi: 10.16182/j.issn1004731x.joss.201706016

基于随机集理论的相关变量模型不确定性量化

赵亮¹, 杨战平²

1.西南科技大学信息工程学院,四川绵阳 621010;
2.电子工程研究所,四川绵阳 621900

收稿日期:2015-07-16 修回日期:2015-11-19 出版日期:2017-06-08 发布日期:2020-06-04
作者简介:赵亮(1983-),男,四川德阳,博士生,研究方向为模型验证与确认、不确定性量化;杨战平(1966-),男,重庆,博士,研究员,博导,研究方向为复杂系统建模与仿真。
基金资助:
西南科技大学博士研究基金(16zx7147),中物院科技发展基金(2012B0403058)

Model Uncertainty Quantification for Dependent Variables Based on Random Set Theory

Zhao Liang¹, Yang Zhanping²

1. Southwest University of Science and Technology, School of Information Engineering, Mianyang 621010, China;
2. Institute of Electronic Engineering, Mianyang 621900, China

Received:2015-07-16 Revised:2015-11-19 Online:2017-06-08 Published:2020-06-04

摘要/Abstract

摘要： 随机集理论为基于模型的系统分析提供了一种统一的不确定性量化框架。针对现有随机集理论模型不确定性量化研究中缺乏对模型变量相关性的考虑这一不足,提出了一种改进的基于随机集理论的不确定性量化方法。该方法根据模型变量间的相关系数信息,通过Nataf变换产生相关随机样本,进而获取多维空间内焦元的联合基本概率分配。由此所得的不确定性量化结果能够在模型变量间存在相关性的情况下正确反映系统状况。数值仿真证明了所提方法的有效性。

关键词: 随机集理论, 不确定性量化, 相关性, Nataf变换

Abstract: Random set theory provides a uniform mechanism for model uncertainty quantification in system analysis. An improved method was proposed based on random set theory for uncertainty quantification considering the dependence among system variables. The Nataf transformation was used to generate dependent random samples to be consistent with correlation coefficients information, and then the joint basic probability assignments for the multidimensional focal elements were calculated to construct the random set. The result of uncertainty quantification based on the random set can reflect the real system response under dependent variables. Simulation results show the presented method rationality.

Key words: random set theory, uncertainty quantification, dependence, Nataf transformation

中图分类号:

TP391.9

赵亮, 杨战平. 基于随机集理论的相关变量模型不确定性量化[J]. 系统仿真学报, 2017, 29(6): 1277-1283.

Zhao Liang, Yang Zhanping. Model Uncertainty Quantification for Dependent Variables Based on Random Set Theory[J]. Journal of System Simulation, 2017, 29(6): 1277-1283.

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基于随机集理论的相关变量模型不确定性量化

Model Uncertainty Quantification for Dependent Variables Based on Random Set Theory

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