NSF Young Investigator

NSF 青年研究员

• 批准号: 9457645
• 负责人: Sanjeev Kulkarni
• 金额: $ 31.25万
• 依托单位: Princeton University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1994
• 资助国家: 美国
• 起止时间: 1994-08-15 至 2000-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9457645&HistoricalAwards=false
• 关键词: NSF; Young; Investigator

The use of adaptive and learing strategies in engineering systems is important for several reasons including increased robustness against changes in operating conditions and errors in data and modeling, reduced need for explicit programming, and improved performance over time. However, although such strategies have been widely used and studied, certain basic questions such as "What are optimal strategies for adaptation?" and "How much data is required?" remain largely unanswered. Therefore, the design of adaptive and learning systems is generally done in an ad hoc manner with little experience gained from on e application to the next. furthermore, very few performance guarantees can be provided for the algorithms used. The goal of this research is to contribute to the theoretical foundations of adaptive and learning systems. Some particular topics to be pursued include the analysis of estimation and adaptive control algorithms under arbitrary disturbances, the study of fundamental limitations in system identification, and understanding the role of prior uncertainty and feedback in determining system performance. The primary goal is to obtain results on the fundamental capabilities and limitations of adaptive and learning systems in order to allow more principled design and analysis of such systems.

在工程系统中使用自适应和学习策略很重要，原因有几个，包括增加对操作条件变化和数据和建模错误的鲁棒性，减少对显式编程的需求，以及随着时间的推移提高性能。 然而，尽管这些战略已被广泛使用和研究，某些基本问题，如“什么是最佳的适应战略？和“需要多少数据？“基本上没有答案 因此，自适应和学习系统的设计通常是在一个特设的方式，从一个应用程序到下一个获得的经验很少。 此外，对于所使用的算法几乎不能提供性能保证。 本研究的目标是为适应性和学习系统的理论基础做出贡献。 要追求的一些特定的主题包括任意干扰下的估计和自适应控制算法的分析，在系统识别的基本限制的研究，并了解在确定系统性能的先验不确定性和反馈的作用。 主要目标是获得自适应和学习系统的基本能力和局限性的结果，以允许更有原则的设计和分析这样的系统。