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Adaptive Control Based on the Use of Collective Information from Multiple Models

Adaptive Control Based on the Use of Collective Information from Multiple Models
基于使用多个模型的集体信息的自适应控制
批准号:
1102178
负责人:
Kumpati Narendra
金额:
$34.82万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2011
资助国家:
美国
项目状态:
已结题
起止时间:
2011-09-01 至 2014-08-31

项目摘要

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中文摘要
翻译
智力价值:该提案的目标是发展基于多种模型的不确定情况下决策的数学理论。应如何结合不同的观点,以便在任何时刻快速、准确地做出决定。过去的努力是基于拥有足够多的专家,并根据短期内被证明最成功的决定作出目前的决定。与这种方法不同的是,该提案涉及新的程序,在该程序中,所有专家的决定都经过加权,以随时选择行动。反过来,权重取决于所有参与者最近的短期表现。模拟研究表明,新方法的决策速度更快、更准确,而且后者在快速变化的环境下也更稳健。更广泛的影响:提案中调查的方法将适用于非常大的一类问题。特别是,这项新技术将在网络物理系统和网络问题中得到应用,在网络物理系统中,大量数据由大量传感器收集,而在网络问题中,分布式决策者必须实时做出关键选择。调查结果将在全国和国际会议上以及在耶鲁大学举办的专门研讨会上由国际和平协会及其同事广泛传播。更重要的是,本科生将在学年和暑假期间接受该方法的数学基础以及在实际问题中的应用方面的培训。
英文摘要
Intellectual Merit:The objective of the proposal is to develop mathematical theory of decision making under uncertainty based on multiple models. How the different views should be combined to arrive at decisions rapidly and accurately at any instant is addressed. Past efforts were based on having a sufficiently large number of experts and basing the current decision on the one proven most successful in the short term. In contrast to such approaches, the proposal deals with novel procedures, in which the decisions of all the experts are weighted to choose the action at any instant. The weights, in turn, depend upon the recent short term performance of all the participants. Simulation studies have indicated that the new approach results in significantly faster and more accurate decisions, and that the latter are more robust even under rapidly changing environments.Broader Impacts:The methodology investigated in the proposal will be applicable to a very large class of problems. In particular, the new technique will find application in Cyber-Physical systems in which vast amounts of data are collected by numerous sensors, and networking problems in which distributed decision makers have to make critical choices in real time. The results obtained from the investigations will be widely disseminated by the PI and his co-workers in national and international conference, and in specialized workshops held at Yale. More importantly, undergraduates will be trained by the PI, both during the academic year and during the summers, in the mathematical foundations of the approach as well as in their application to practical problems.
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会议论文
Collaborative Research: Mutual Learning: A Systems Theoretic Investigation
  • 批准号:
    1930601
  • 项目类别:
    Standard Grant
  • 资助金额:
    $44.63万
  • 财政年份:
    2019
  • 负责人:
    Kumpati Narendra
  • 依托单位:
How to adapt efficiently using distributed resources and multiple models to time varing dynamic systems
  • 批准号:
    1503751
  • 项目类别:
    Standard Grant
  • 资助金额:
    $35.88万
  • 财政年份:
    2015
  • 负责人:
    Kumpati Narendra
  • 依托单位:
Collaborative Research: Fast reinforcement learning using multiple models and state decompositions for apllications to Plug-in Hybrid Vehicles
  • 批准号:
    1408279
  • 项目类别:
    Standard Grant
  • 资助金额:
    $20.0万
  • 财政年份:
    2014
  • 负责人:
    Kumpati Narendra
  • 依托单位:
Adaptive Control of Time-Varying Systems Using Multiple Models
  • 批准号:
    0824118
  • 项目类别:
    Standard Grant
  • 资助金额:
    $39.27万
  • 财政年份:
    2008
  • 负责人:
    Kumpati Narendra
  • 依托单位:
国内基金
海外基金
Cortical control of internal state in the insular cortex-claustrum region