Stochastic Prediction for the Design and Management of Interacting Complex Systems

交互复杂系统设计和管理的随机预测

• 批准号: 1025043
• 负责人: Roger Ghanem
• 金额: $ 31万
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-15 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1025043&HistoricalAwards=false
• 关键词: Stochastic; Prediction; Design; Management; Interacting; Stochastic; Prediction; Design; Management; Interacting

This research considers systems that combine social networks and sensor networks with complicated interactions involving hierarchical structure. To design and analyze such complex distributed systems, information-theoretical methods will be combined with advanced Bayesian and non-Bayesian statistical techniques such as spatiotemporal nonlinear estimation and prediction, advanced changepoint detection and estimation methods, and multihypothesis decision-making strategies. Advanced data fusion methods at different levels will be proposed and tested. Distributed networks will be modeled by quite general coupled fractional hidden Markov models adapted to allow for nonlinear prediction and detection-classification. The spaces in which estimation, prediction, and classification are performed may be both metric and symbolic, thus allowing for the effective incorporation of sensor (metric) and social (symbolic) networks as a part of a large-scale distributed system. Design of complex multi-level hierarchical systems, including systems that search for patterns to identify developing or immediate threats, is vitally important for various areas, including national security, environmental monitoring, SmartGrid and other critical infrastructures. This research will develop novel approaches for automated efficient fusion of information from multiple sources or/and from multiple levels of hierarchy of complex systems that will enable these systems to achieve high accuracy in detection of changes in trends, prediction, and recognition. Probabilistic modeling of networks will be coupled with novel approaches to event pattern recognition, change detection and information integration/fusion in complex, multisource-multisensor distributed heterogeneous systems. Both mathematical formulations and solution algorithms will be developed.

这项研究考虑了将社交网络和传感器网络与涉及层次结构的复杂互动相结合的系统。为了设计和分析此类复杂的分布式系统，信息理论方法将与先进的贝叶斯和非贝叶斯统计技术相结合，例如时空的非线性估计和预测，高级更改点检测和估计方法以及多动型决策策略。将提出和测试不同级别的高级数据融合方法。分布式网络将通过相当通用的耦合分数隐藏的马尔可夫模型来建模，以允许进行非线性预测和检测分类。 进行估计，预测和分类的空间可能既可以是度量标准又可以是符号符号，从而使传感器（度量）和社交（符号）网络有效地纳入大规模分布式系统的一部分。对于各个领域，包括国家安全，环境监测，SmartGrid和其他关键基础架构，设计要识别开发或直接威胁的模式的系统，包括寻找模式或直接威胁的模式的系统，包括寻找模式或直接威胁的系统。这项研究将开发出新的方法，以从多个来源或/和来自复杂系统的多个层次层次结构的信息自动融合，这将使这些系统能够在检测趋势，预测和识别的变化方面达到高度准确性。 网络的概率建模将与复杂的多源 - 材料传感器分布式异质系统中事件模式识别，变化检测和信息集成/融合的新颖方法相结合。将开发数学配方和解决方案算法。