Application of global sensitivity analysis for complexity reduction, parameter estimation and time series forecasting

应用全局敏感性分析来降低复杂性、参数估计和时间序列预测

• 批准号: EP/D506743/1
• 负责人: Nilay Shah
• 金额: $ 54.15万
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2006
• 资助国家: 英国
• 起止时间: 2006 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FD506743%2F1
• 关键词: Application; global; sensitivity; analysis; complexity

Modern processes are so complex that physical experimentation is too time-consuming, too expensive or even impossible. Mathematical or computational models are developed to approximate such processes. Complex models are finding many new and important applications in a variety of engineering spheres, including biosystems, process systems and supply chains. Good modelling practice requires sensitivity analysis (SA) to ensure the model quality by analysing the model structure, selecting the best type of model and effectively identifying the important model parameters. The Sobol' method of global sensitivity indices is superior to other SA methods. It can be applied to any type of models for quantifying and reducing problem complexity without sacrificing accuracy and it is not dependent on a nominal point or differentiability of the functions. However, it has been applied only to low scale models because of the computational limitations of the existing technique. We propose a generalization of the Sobol' method based on efficient high dimensional Quasi Monte Carlo sampling and the advanced high dimensional model representation technique. It will enable to analyse and solve practical large scale problems. By combining the generalized Sobol' method and our novel global optimisation method we will develop a new technique for parameter estimation with improved accuracy. Furthermore, since accurate forecasting is critical in economics, engineering, revenue and supply chain management, we propose to develop a novel approach for identifying the most efficient time series forecasting models for different data sets.

现代过程是如此复杂，以至于物理实验太耗时，太昂贵，甚至不可能。开发数学或计算模型来近似这种过程。复杂模型在生物系统、过程系统和供应链等各种工程领域中有着许多新的重要应用。良好的建模实践需要敏感性分析，通过分析模型结构、选择最佳模型类型和有效识别重要模型参数来确保模型质量。全局灵敏度指标的Sobol方法上级其它SA方法。它可以应用于任何类型的模型，用于量化和减少问题的复杂性，而不牺牲精度，它不依赖于一个名义点或可微的功能。然而，由于现有技术的计算限制，它仅适用于低比例尺模型。我们提出了一个推广的Sobol'方法的基础上有效的高维拟蒙特卡罗抽样和先进的高维模型表示技术。它将能够分析和解决实际的大规模问题。通过结合广义Sobol'方法和我们的新的全局优化方法，我们将开发一种新的技术参数估计精度提高。此外，由于准确的预测是经济，工程，收入和供应链管理的关键，我们建议开发一种新的方法来确定最有效的时间序列预测模型，为不同的数据集。

项目成果

17th European Symposium on Computer Aided Process Engineering

第十七届欧洲计算机辅助过程工程研讨会

• DOI: 10.1016/s1570-7946(07)80034-4
• 发表时间: 2007
• 作者: Rodriguez-Fernandez M

Derivative based global sensitivity measures and their link with global sensitivity indices

• DOI: 10.1016/j.matcom.2009.01.023
• 发表时间: 2009-06-01
• 期刊: MATHEMATICS AND COMPUTERS IN SIMULATION
• 影响因子: 4.6
• 作者: Sobol, I. M.;Kucherenko, S.
• 通讯作者: Kucherenko, S.

Global Sensitivity Analysis Challenges in Biological Systems Modeling

• DOI: 10.1021/ie900139x
• 发表时间: 2009-08-05
• 期刊: INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH
• 影响因子: 4.2
• 作者: Kiparissides, A.;Kucherenko, S. S.;Pistikopoulos, E. N.
• 通讯作者: Pistikopoulos, E. N.

Optimal experiment design based on global sensitivty analysis

基于全局敏感性分析的优化实验设计

• 发表时间: 2007
• 作者: M Rodriguez-Fernandez

The Importance of being Global.Application of Global Sensitivity Analysis in Monte Carlo option Pricing

全球化的重要性。全球敏感性分析在蒙特卡罗期权定价中的应用

• 发表时间: 2007
• 期刊: Wilmott
• 作者: Kucherenko S