Two improved methods for variance-based global sensitivity analysis' W-indices

被引：0

作者：

Gong X. ^{[1
]}

Lyu Z. ^{[1
]}

Zuo J. ^{[1
]}

机构：

[1] School of Aeronautics, Northwestern Polytechnical University, Xi'an

来源：

Hangkong Xuebao/Acta Aeronautica et Astronautica Sinica | 2016年 / 37卷 / 06期

基金：

中国国家自然科学基金;

关键词：

Advanced Monte Carlo simulation method; Global sensitivity analysis; Sobol's indices; Sparse grid integration; W-indices;

D O I：

10.7527/S1000-6893.2016.0090

中图分类号：

学科分类号：

摘要：

In the global sensitivity analysis (SA), the variance-based sensitivity indices, such as Sobol's indices and W-indices, are used widely. Sobol's indices estimate the average variation of model output when input variables are fixed in their points. W-indices measure the average reduction of model output if input variables are reduced in their distributions. Compared with Sobol's indices, W-indices include more information. The double loop repeated set Monte Carlo (DLRS MC) and double loop single set Monte Carlo (DLSS MC) are two traditional methods, but these available methods for solving W-indices are still defective. In order to calculate W-indices efficiently, two new methods are presented. They are advanced Monte Carlo simulation (AMCS) and sparse grid integration (SGI)-based method. The AMCS only needs one set of samples to estimate all W-indices. Since screening method is used to estimate the variance in the conditional interval, and the count error induced by taking an integer in DLSS MC can be avoided, the accuracy of AMCS is higher than that of DLSS MC. The SGI-based method estimates W-indices by evaluating threefold statistic moment by SGI, in which the efficiency of the SGI is inherited. Finally, two numerical examples and an engineering example are employed to demonstrate the reasonability and efficiency of the presented methods. © 2016, Press of Chinese Journal of Aeronautics. All right reserved.

引用

页码：1888 / 1898

页数：10

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