Model Uncertainty Quantification for Dependent Variables Based on Random Set Theory

Authors

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

Abstract

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.

Recommended Citation

Liang, Zhao and Yang, Zhanping (2020) "Model Uncertainty Quantification for Dependent Variables Based on Random Set Theory," Journal of System Simulation: Vol. 29: Iss. 6, Article 16.
DOI: 10.16182/j.issn1004731x.joss.201706016
Available at: https://dc-china-simulation.researchcommons.org/journal/vol29/iss6/16

First Page

1277

Revised Date

2015-11-19

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.201706016

Last Page

1283

CLC

TP391.9

Recommended Citation

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.

DOI

10.16182/j.issn1004731x.joss.201706016