报告题目：Adaptive Approximation Based Feedback Control and Fault Diagnosis（基于自适应逼近的反馈控制与故障诊断）

报告人：Prof. Marios Polycarpou，IEEE Fellow

报告时间：2006年6月5日 下午16：00

报告地点：FIT楼1-312

主办单位：清华大学计算机系智能技术与系统国家重点实验室

内容简介：

(1)报告内容：

Recent technological advances in computing hardware, communications and real-time software have provided the infrastructure for designing intelligent decision and control systems. Based on current trends, high performance feedback systems of the future will require greater autonomy in a number of frontiers. First, they need to be able to deal with greater levels of, possibly, time-varying uncertainty. Second, they need to be able to handle uncertainties in the environment, which will allow the feedback system to be more flexible in dealing with unanticipated events such as faults, obstacles and disturbances. Finally, key advances in distributed and mobile computing will allow for exciting possibilities in distributed decision making and control by agent-type systems. This will require feedback systems to operate in distributed environments with cooperative capabilities. 

One of the key tools for realizing such advances in the performance and autonomy of feedback systems is "learning." Feedback systems with learning capabilities can potentially help reduce modeling uncertainty adaptively, make feedback systems more "intelligent" in the presence of uncertainty in the environment, and initiate design methods for cooperative feedback systems in distributed environments. During the last decade there has been a variety of learning techniques developed for feedback systems, based on structures such as neural networks, fuzzy systems, wavelets, etc. The goal of this presentation is to provide a unifying framework for designing and analyzing feedback systems with learning capabilities. Various adaptive approximation techniques and learning algorithms will be presented and illustrated, and directions for future research will be discussed.

(2)报告人简介：

Dr. Marios Polycarpou received the Ph.D. degree in Electrical Engineering from the University of Southern California, Los Angeles, CA, in 1992. He then joined the University of Cincinnati, Ohio, where he became a Full Professor of Electrical and Computer Engineering and Computer Science, before he moved to the newly established Department of Electrical and Computer Engineering at the University of Cyprus in2001, where he is currently a Professor and Interim Dept. Head.

Dr. Polycarpou was the recipient of the William H. Middendorf Research Excellence Award at the University of Cincinnati (1997). He is an Associate Editor of two international journals and past Associate Editor of the IEEE Transactions on Neural Networks (1998-2003) and of the IEEE Transactions on Automatic Control (1999-2002). He served as the Chair of the Technical Committee on Intelligent Control of the IEEE Control Systems Society (2003-05) and as Vice President, Conferences, of the IEEE Computational Intelligence Society (2002-03).

He has been invited as Keynote Plenary Speaker at several international conferences and served as General Chair of the joint 2005 IEEE International Symposium on Intelligent Control and Mediterranean Conference on Control and Automation. Dr Polycarpou is currently the Editor-in-Chief of the IEEE Transactions on Neural

Networks.

Dr. Polycarpou has published more than 150 articles in refereed journals, edited books and refereed conference proceedings, and co-authored a book Adaptive Approximation Based Control. He has 3 patents. He is a Fellow of the IEEE.