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【学术报告】Solution to the General Nonlinear Filtering Problems Based on Recurrent Neural Network

发布日期:2024-03-21    点击:


4556银河国际统计与运筹系

学术报告

Solution to the General Nonlinear Filtering Problems Based on Recurrent Neural Network

陈秀琼 讲师

(中国人民大学)

报告时间: 2024326 (星期) 下午4:00-5:00


报告地点 E706


报告摘要:The famous filtering problem of estimating the state of a stochastic dynamical system from noisy observations is of central importance in engineering, and high-dimensional nonlinear filtering is still a challenging problem. This problem is reduced to solving the Duncan-Mortensen-Zakai (DMZ) equation which is satisfied by the unnormalized conditional density of the state given the observation history. For general nonlinear filtering problems, we leverage on the representation ability of recurrent neural network and provide a computationally efficient and optimal framework for nonlinear filter design based on Yau-Yau algorithm and recurrent neural network.


报告人简介:陈秀琼,现任中国人民大学数学学院讲师2014年于4556银河国际获学士学位2019年于清华大学数学科学系获得博士学位博士后入选清华大学“水木学者”计划主要研究方向为控制论与非线性滤波,曾在IEEE Transactions on Automatic Control和IEEE Transactions on Neural Networks and Learning Systems等国际控制领域期刊上发表论文现主持国家自然科学基金青年基金一项


邀请人: 罗雪



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