A general first-order global sensitivity analysis method
A general first-order global sensitivity analysis method
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DOI：
10.1016/j.ress.2007.04.001
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发表时间：
2008-07-01
影响因子：
8.1
通讯作者：
Gertner, George Zdzislaw
中科院分区：
文献类型：
--
作者：
Xu, Chonggang;Gertner, George Zdzislaw
傅立叶幅值灵敏度检验（FAST）是最流行的全局灵敏度分析技术之一。FAST的主要机制是通过搜索功能为每个参数分配一个特征频率。然后，对于一个特定的参数，方差的贡献，可以挑选出的模型输出的特征频率。虽然FAST已被广泛应用，但它有两个局限性：（1）使用整数特征频率的参数之间的混叠效应;（2）仅适用于具有独立参数的模型。在本文中，我们综合了克服混叠效应限制的改进[Tarantola S，Gatelli D，Mara TA.一阶整体灵敏度指标估计的随机平衡设计。Reliab Eng Syst Safety 2006; 91（6）：717-27]和克服独立性限制的改进[Xu C，Gertner G.将全局灵敏度分析技术扩展到具有相关参数的模型。Comput Stat Data Anal 2007，接受出版]。通过这种方式，FAST可以成为线性/非线性模型的通用一阶全局灵敏度分析方法，其相关/不相关参数可以由用户指定。我们将一般FAST应用于四个相关参数的测试用例。结果表明，广义FAST方法得到的灵敏度指标与相关比法得到的灵敏度指标吻合较好，相关比法是一种非参数方法，适用于参数相关的模型。(C)2007爱思唯尔有限公司保留所有权利。
Fourier amplitude sensitivity test (FAST) is one of the most popular global sensitivity analysis techniques. The main mechanism of FAST is to assign each parameter with a characteristic frequency through a search function. Then, for a specific parameter, the variance contribution can be singled out of the model output by the characteristic frequency. Although FAST has been widely applied, there are two limitations: (1) the aliasing effect among parameters by using integer characteristic frequencies and (2) the suitability for only models with independent parameters. In this paper, we synthesize the improvement to overcome the aliasing effect limitation [Tarantola S, Gatelli D, Mara TA. Random balance designs for the estimation of first order global sensitivity indices. Reliab Eng Syst Safety 2006; 91(6):717-27] and the improvement to overcome the independence limitation [Xu C, Gertner G. Extending a global sensitivity analysis technique to models with correlated parameters. Comput Stat Data Anal 2007, accepted for publication]. In this way, FAST can be a general first-order global sensitivity analysis method for linear/nonlinear models with as many correlated/uncorrelated parameters as the user specifies. We apply the general FAST to four test cases with correlated parameters. The results show that the sensitivity indices derived by the general FAST are in good agreement with the sensitivity indices derived by the correlation ratio method, which is a non-parametric method for models with correlated parameters. (C) 2007 Elsevier Ltd. All rights reserved.