Model Uncertainty Quantification for Dependent Variables Based on Random Set Theory

doi:10.16182/j.issn1004731x.joss.201706016

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.

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

CLC Number:

TP391.9

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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