Wenxuan Wang*, Xiaoyi Wang
CMES-Computer Modeling in Engineering & Sciences, Vol.124, No.3, pp. 985-1000, 2020, DOI:10.32604/cmes.2020.09006
Abstract The importance analysis method represents a powerful tool for quantifying the impact of input uncertainty on the output uncertainty. When an input
variable is described by a specific interval rather than a certain probability distribution, the interval importance measure of input interval variable can be calculated by the traditional non-probabilistic importance analysis methods.
Generally, the non-probabilistic importance analysis methods involve the Monte
Carlo simulation (MCS) and the optimization-based methods, which both have
high computational cost. In order to overcome this problem, this study proposes
an interval important analytical method avoids the time-consuming optimization
process. First, the original performance function is… More >
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