台湾科技大学苏顺峰教授学术报告通知

发布时间:2017-01-11  浏览次数:17

 台湾科技大学苏顺峰教授将于1月13号上午9点在自动化学院4楼会议室(61号楼4172)举行学术报告会,欢迎广大师生前来参加。报告信息如下:


TitleLearning Control: Ideas and Problems in Adaptive Fuzzy Control

AbstractIntelligent control is a promising way of control design in recent decades. Intelligent control design usually needs some knowledge of the system considered. However, such knowledge usually may not be available. Learning becomes an important mechanism for acquiring such knowledge. Learning control seems a good idea for control design for unknown or uncertain systems. To learn controllers is always a good idea, but somehow like a dream. It is because learning is to learn from something. But when there is no good controller, where to learn from? Nevertheless, there still exist approaches, such as adaptive fuzzy control, that can facilitate such an idea. It is called performance based learning (reinforcement learning and Lyapunov stability). This talk is to discuss fundamental ideas and problems in one learning controller -- adaptive fuzzy control. Some deficits of such an approach are discussed.The idea is simple and can be extended to various learning mechanisms. In fact, such an idea can also be employed in various learning control schemes. If you want to use such kind of approaches, those issues must be considered in your study.

IntroductionShun-Feng Su received the B.S. degree in electrical engineering from National Taiwan University, Taiwan, R.O.C., in 1983, and the M.S. and Ph.D. degrees in electrical engineering from Purdue University, West Lafayette, Indiana, in 1989 and 1991, respectively. He is now a chair Professor in the Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan. He is an IEEE Fellow and also a CACS Fellow. He has published more than 160 refereed journals and conference papers in the areas of robotics, intelligent control, fuzzy systems, neural networks, and non-derivative optimization. His current research interests include neural networks, fuzzy modeling, machine learning, virtual reality simulation, and intelligent transportation systems.




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