TY - EJOU
AU - Wang, Wenxuan
AU - Wang, Xiaoyi
TI - Subinterval Decomposition-Based Interval Importance Analysis Method
T2 - Computer Modeling in Engineering \& Sciences
PY - 2020
VL - 124
IS - 3
SN - 1526-1506
AB - 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 decomposed into a combination of a series of one-dimensional subsystems. Next, the interval of each variable
is divided into several subintervals, and the response value of each one-dimensional
subsystem at a specific input point is calculated. Then, the obtained responses are
taken as specific values of the new input variable, and the interval importance is
calculated by the approximated performance function. Compared with the traditional non-probabilistic importance analysis method, the proposed method signifi-
cantly reduces the computational cost caused by the MCS and optimization
process. In the proposed method, the number of function evaluations is equal to
one plus the sum of the subintervals of all of the variables. The efficiency and accuracy of the proposed method are verified by five examples. The results show that
the proposed method is not only efficient but also accurate.
KW - Importance analysis method; interval variable; subinterval decomposition; performance function; MCS
DO - 10.32604/cmes.2020.09006