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学术报告:2019年1月3日, 上午10:15-11:15,Shuai Li

编辑:www.402.com  发布时间:2019-01-01浏览次数:271

永利皇宫数学机构邀请专家申请表


报告人

Shuai Li

单位

The Hong Kong Polytechnic University

报告题目

Distributed Winner-Take-All and Biased Min-Consensus: Two New Schemes for Network Dynamics

报告时间

201913日,上午1015-1115

地点

九龙湖校区永利皇宫第一报告厅

邀请人

温广辉,张凡,虞文武

报告摘要


Distributed consensus has been intensively studied in recent years as a means to mitigate state differences among dynamic nodes on a graph. It has been successfully employed in various applications, e.g., formation control of multi-robots, load balancing, and clock synchronization. However, almost all the existing applications cast an impression of consensus as a simple process to iteratively reach agreement, without any clue on possibility to generate advanced complexity, e.g., emergence of dominance, emergence of intelligent behavior, like distributed path planning. Counterintuitively, in this talk I will show that the opposite side of agreement, which is the dominance by one leading node, can be achieved by distributed iteration, and the complexity of shortest path planning can emerge from a perturbed version of a min-consensus protocol. Theoretical proof and simulation studies will be presented to support the conclusions.

报告人简介

Shuai Li received the B.E. degree in Precision Mechanical Engineering from Hefei University of Technology, China, in 2005, the M.E. degree in Automatic Control Engineering from University of Science and Technology of China, China, in 2008, and the Ph.D. degree in Electrical and Computer Engineering from Stevens Institute of Technology, Hoboken, NJ, USA, in 2014. He is currently a professor with Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China. His current research interests include dynamic neural networks, wireless sensor networks, robotic networks, machine learning, and other dynamic problems defined on a graph. Dr. Li is currently on the editorial board of the Neural Computing and Applications and the International Journal of Distributed Sensor Networks.