Nonparametric Nonlinear Adaptive Detection and Estimation

非参数非线性自适应检测和估计

• 批准号: 0622239
• 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: 2006
• 资助国家: 美国
• 起止时间: 2006-10-01 至 2009-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0622239&HistoricalAwards=false
• 关键词: Nonparametric; Nonlinear; Adaptive; Detection; Estimation

NSF-ECS Proposal 0622239Nonparametric Nonlinear Adaptive Detection and EstimationObjectives and approaches: The objective of this research is to create a unifying nonlinear information processing framework to handle increasingly complex modeling and data analysis problems encountered in science and engineering. The proposed framework exploits and unifies three concepts: reproducing kernel Hilbert spaces, nonparametric kernel density estimation, and information theoretic optimality measures. Developed techniques will be illustrated in a brain interface, in which high dimensional spatiotemporal electroencelaphogram activity will be translated to intended commands for a robot. This interface will be designed to facilitate fusion with myoelectrically controlled neural prostheses.Intellectual merit: Conventional techniques relying on nonlinear programming of semiparametric models with low-order statistical criteria, which is prone to various difficulties including model complexity selection and existence of suboptimal solutions, cannot cope with the challenges of increasingly complex engineering problems. Proposed work facilitates the adaptation of nonlinear models exploiting smooth generalized linear models and principled information processing in a novel unified framework. This will advance the state-of-the-art in nonlinear adaptive signal processing and machine learning. The brain interface testbed will facilitate future collaboration between two currently disconnected neural prostheses communities.Broader impacts: The theoretical framework will impact signal processing and machine learning, thus will have immediate influence on fields that rely heavily on statistical modeling, data analysis, and processing techniques. Specifically, contributions to the emerging neural engineering field will impact the design of future neural prostheses, as well as fundamental brain and cognition research. The project will help educate graduate engineers with mathematical rigor and an interdisciplinary focus.

NSF-ECS提案0622239非参数非线性自适应检测和估计目标和方法：本研究的目标是创建一个统一的非线性信息处理框架，以处理科学和工程中遇到的日益复杂的建模和数据分析问题。该框架利用并统一了三个概念：再生核希尔伯特空间，非参数核密度估计和信息论最优性措施。发达国家的技术将说明在大脑接口，其中高维时空脑电图活动将被翻译为机器人的预期命令。这个接口将被设计为促进融合与myoelectrically控制的神经prosthesies.Intellectual优点：传统的技术依赖于非线性规划的半参数模型与低阶统计准则，这是容易出现各种困难，包括模型的复杂性选择和次优解的存在，不能科普日益复杂的工程问题的挑战。建议的工作有利于适应非线性模型，利用光滑的广义线性模型和原则的信息处理在一个新的统一框架。这将推动非线性自适应信号处理和机器学习的发展。更广泛的影响：理论框架将影响信号处理和机器学习，从而对严重依赖统计建模、数据分析和处理技术的领域产生直接影响。具体来说，对新兴神经工程领域的贡献将影响未来神经假体的设计，以及基础的大脑和认知研究。该项目将有助于培养具有数学严谨性和跨学科重点的工程师。