Robust Information Filtering Techniques for Static and Dynamic State Estimation

用于静态和动态估计的鲁棒信息过滤技术

• 批准号: 0524835
• 负责人: Deniz Erdogmus
• 金额: $ 24万
• 依托单位: Oregon Health & Science University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-10-01 至 2009-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0524835&HistoricalAwards=false
• 关键词: Robust; Information; Filtering; Techniques; Static

Proposal Number: ECS-0524835Proposal Title: Robust Information Filtering Techniques for Static and Dynamic State EstimationPI Name: Ergodmus, DenizPI Institution: Oregon Health and Science University Intellectual merit: This project addresses the well-established challenge of "state estimation," of estimating the current state of variables in a complex system which are not observed directly. Previous work in systems theory has developed well-perfected methods for systems whose variables are all continuous, or all discrete, under conditions where the dynamics of the system itself are already perfectly known. This team proposes a fundamental advance, by unifying recent breakthroughs addressing the challenge of what to do when the dynamical system is not perfectly known, in the general nonlinear case. The recent work to be drawn upon involves information-theoretic learning, sigma-point filtering, particle filtering concepts, and the use of recurrent neural networks and backpropagation through time (which offer major advantages as universal approximators of nonlinear dynamical systems).Broader Benefits: Better state estimation will be important to all kinds of challenges in managing complex systems more effectively, whether by neural networks or other components. The testbed to be used here- the localization of elderly patients in a prototype advanced health care clinic - was chosen both for its value as a challenge to the basic research and for its promise as a starting point for large real-world benefits.The educational benefits included cross-disciplinary education (highly credible, given the team and the project) plus more standard sorts of benefits to education.

提案号：ecs -0524835提案标题：静态和动态状态估计的鲁棒信息过滤技术名称：Ergodmus， DenizPI机构：俄勒冈健康与科学大学智力优点：该项目解决了“状态估计”的公认挑战，即估计复杂系统中未直接观察到的变量的当前状态。在系统理论中，以前的工作已经开发了非常完善的方法，用于在系统本身的动力学已经完全已知的条件下，其变量全部连续或全部离散的系统。这个团队提出了一个根本性的进步，通过统一最近的突破来解决在一般非线性情况下，当动力系统不完全已知时该怎么做的挑战。最近的工作涉及信息理论学习，西格玛点滤波，粒子滤波概念，以及循环神经网络和随时间反向传播的使用（作为非线性动态系统的通用逼近器提供了主要优势）。更广泛的好处：更好的状态估计对于更有效地管理复杂系统的各种挑战都很重要，无论是通过神经网络还是其他组件。这里使用的测试平台——在一个原型高级医疗保健诊所中对老年患者进行本地化——被选中，既是因为它作为对基础研究的挑战的价值，也是因为它有望作为一个起点，为现实世界带来巨大的好处。教育方面的好处包括跨学科的教育（考虑到团队和项目，这是高度可信的）以及更多标准的教育方面的好处。