Stochastic Prediction for the Design and Management of Interacting Complex Systems

本研究考虑联合收割机结合社交网络和传感器网络的系统，涉及层次结构的复杂交互。为了设计和分析这种复杂的分布式系统，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，如时空非线性估计和预测，先进的变点检测和估计方法，以及多假设决策策略。将提出和测试不同级别的先进数据融合方法。分布式网络将建模相当一般的耦合分数隐马尔可夫模型，适合允许非线性预测和检测分类。 执行估计、预测和分类的空间可以是度量的和符号的，从而允许将传感器（度量）和社交（符号）网络有效地结合为大规模分布式系统的一部分。设计复杂的多级分层系统，包括搜索模式以识别发展中或直接威胁的系统，对于包括国家安全，环境监测，智能电网和其他关键基础设施在内的各个领域至关重要。这项研究将开发新的方法，用于自动有效地融合来自多个来源或/和复杂系统的多个层次结构的信息，使这些系统能够在检测趋势变化、预测和识别方面实现高准确度。 网络的概率建模将与事件模式识别，变化检测和信息集成/融合在复杂的，多源多传感器分布式异构系统的新方法相结合。数学公式和解决方案的算法将被开发。

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.