Monday, July 14

3:15 PM-5:15 PM
Building 160, Room 161j

MS11 Recursive Methods in Filtering and Estimation

The minisymposium will highlight current research on the development of recursive algorithms that exhibit improved performance in terms of accuracy, robustness to unmodelled dynamics, and robustness to insufficient prior information. It is becoming increasingly relevant to design recursive algorithms that are more tolerant to modeling and numerical errors. This is especially true with the increasing complexity of the problems arising in modern applications. The talks in this minisymposium will describe several recent methods developed for this purpose. The talks in this minisymposium will touch upon issues that arise in numerical linear algebra, recursive filtering and estimation, and structured computations.

Organizers: Ming Gu and Ali H. Sayed, University of California, Los Angeles; and S. Chandrasekaran, University of California, Santa Barbara

3:15 Reduced Order Identification and Partial Model Matching
Phillip A. Regalia, Institut National des Telecommunications, France
(Cancelled) 3:45 Overview of Recent Results on Q-Learning by George Cybenko, Dartmouth College
(Replaced by) Robustness and Stability of Downdating Algorithms
Michael Stewart, University of California, Los Angeles
4:15 On Equalization and H infinity Filtering
Babak Hassibi and Thomas Kailath, Stanford University
4:45 Rapid Updating of Structured Problems in Filtering
S. Chandrasekaran, Ming Gu, and Ali H. Sayed, Organizers

AN97 Homepage | Program Updates|
Registration | Hotel and Dormitory Information | Transportation | Program-at-a-Glance | Program Overview

MMD, 3/27/97
tjf, 5/27/97
MMD, 7/3/97