Use of Sobol's quasirandom sequence generator for integration of modified uncertainty importance measure
Use of Sobol's quasirandom sequence generator for integration of modified uncertainty importance measure
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DOI：
10.3327/jnst.32.1164
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发表时间：
1995-11-01
影响因子：
1.2
通讯作者：
Saltelli, A
中科院分区：
文献类型：
--
作者：
Homma, T;Saltelli, A
模型输出的敏感性分析与许多实践相关，包括模型验证和计算机代码质量保证。它涉及有影响的模型参数的识别，特别是在具有许多不确定输入变量的计算机程序中实现的复杂模型中。在最近的一篇文章中，Hora 和 Iman 建议的基于不确定性重要性度量的等级变换的名为 HIM* 的新灵敏度分析方法被证明对于执行模型输出的自动灵敏度分析非常强大，即使存在模型非单调性也是如此。其他广泛使用的非参数技术（例如标准化排名回归系数）的情况并非如此。 HIM* 方法的一个缺点是其估计所需的随机样本维数很大，这使得 HIM* 对于具有大量不确定参数的系统不切实际。在本说明中，基于 Sobol 的拟随机生成器的更有效的采样算法与 HIM* 相结合，从而大大减少了有效识别影响变量所需的样本量。针对两种不同的基准测试了新技术的性能。
Sensitivity analysis of model output is relevant to a number of practices, including verification of models and computer code quality assurance. It deals with the identification of influential model parameters, especially in complex models implemented in computer programs with many uncertain input variables. In a recent article a new method for sensitivity analysis, named HIM* based on a rank transformation of the uncertainty importance measure suggested by Hora and Iman was proved very powerful for performing automated sensitivity analysis of model output, even in presence of model non-monotonicity. The same was not true of other widely used nonparametric techniques such as standardized rank regression coefficients. A drawback of the HIM* method was the large dimension of the stochastic sample needed for its estimation, which made HIM* impracticable for systems with large number of uncertain parameters. In the present note a more effective sampling algorithm, based on Sobol's quasirandom generator is coupled with HIM*, thereby greatly reducing the sample size needed for an effective identification of influential variables. The performances of the new technique are investigated for two different benchmarks.