學(xué)術(shù)報告
報告題目：Deep Filtering
報告時間：2022年12月13日，上午10點，騰訊會議：210 900 466
報告摘要：A fundamental problem in control and systems theory is concerned with nonlinear filtering. In the 1960s, celebrated results on nonlinear filtering were obtained. Nevertheless, the computational issues for nonlinear filtering remained to be a long-standing and challenging problem. In this talk, in lieu of treating an infinite dimensional problem for obtaining the conditional distribution, or conditional measure, we are seeking to construct finite-dimensional approximations using deep neural networks for the optimal weights. Two recursions are used in the algorithm. One of them is the approximation of the optimal weight and the other is for approximating the optimal learning rate. [This is a joint work with H. Qian, and Q. Zhang.]
報告人：G. Yin （殷剛）
報告人簡歷： George Yin received the B.S. degree in mathematics from the University of Delaware in 1983, and the M.S. degree in electrical engineering and the Ph.D. degree in applied mathematics from Brown University in 1987. He joined the Department of Mathematics, Wayne State University in 1987, and became Professor in 1996 and University Distinguished Professor in 2017. He moved to the University of Connecticut in 2020. His research interests include stochastic processes, stochastic systems theory, and applications. He was Chair of the SIAM Activity Group on Control and Systems Theory, and was Co-chair of a number of conferences; he served on the Board of Directors of the American Automatic Control Council. He is the Editor-in-Chief of the SIAM Journal on Control and Optimization. He serves on (or served on) editorial boards of over 20 journals and book series including Automatica 1995-2011, IEEE Transactions on Automatic Control 1994-1998, and IEEE Control Systems Letters 2017-2019. He is a Fellow of IEEE, Fellow of IFAC, and Fellow of SIAM.
