Nonparametric Nonlinear Adaptive Detection and Estimation

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Hilbert空间、非参数核密度估计和信息论最优性度量。开发的技术将在大脑界面中进行说明，在该界面中，高维时空脑电活动将被转换为机器人预期的命令。该接口的设计将有助于与肌电控制的神经假体的融合。智能优势：传统技术依赖于具有低阶统计准则的半参数模型的非线性规划，容易出现模型复杂性选择和次优解的存在等各种困难，无法应对日益复杂的工程问题的挑战。所提出的工作促进了非线性模型的适应，利用光滑的广义线性模型和在新的统一框架中的原则性信息处理。这将推动非线性自适应信号处理和机器学习的最新发展。脑接口试验台将促进两个目前分离的神经假体社区之间未来的合作。广泛的影响：理论框架将影响信号处理和机器学习，从而将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。具体地说，对新兴神经工程领域的贡献将影响未来神经假体的设计，以及基础脑和认知研究。该项目将帮助培养具有数学严谨性和跨学科重点的研究生工程师。

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.