The State of the of Art of Neurodynamic Optimization – Past, Present, and Prospect

Speaker:Professor Jun WANG, The Chinese University of Hong Kong

Date:2014-4-18(Friday), 4:30 PM

Location :Room 206, Teaching Building, Haiyun Park, Xiamen University 

Abstract:In this talk, starting with the idea and motivation of neurodynamic optimization, we willreview the historic review and present the state of the art of neurodynamic optimizationwith many models and selected applications. Theoretical results about the state stability,output convergence, and solution optimality of the neurodynamic optimization modelswill be given along with many illustrative examples and simulation results. Four classesof neurodynamic optimization model design methodologies (i.e., penalty methods,Lagrange methods, duality methods, and optimality methods) will be delineated withdiscussions of their characteristics. In addition, it will be shown that many real-timecomputational optimization problems in information processing, system control, androbotics (e.g., parallel data selection and sorting, robust pole assignment in linearfeedback control systems, robust model predictive control for nonlinear systems,collision-free motion planning and control of kinematically redundant robot manipulatorswith or without torque optimization, and grasping force optimization of multi-fingeredrobotic hands) can be solved by means of neurodynamic optimization. Finally,prospective future research directions will be discussed.

Bio:Jun Wang is a Professor and the Director of the Computational Intelligence Laboratory at the Chinese University of Hong Kong. He published over 170 journal papers, 15 book chapters, 11 edited books, and numerous conference papers in these areas. He is the Editor-in-Chief of the IEEE Transactions onCybernetics since 2014 and a member of the editorial board of Neural Networks since 2012. He has been an IEEE Computational Intelligence Society Distinguished Lecturer (2010-2012, 2014-2016). In addition, he served as President of Asia Pacific Neural Network Assembly (APNNA) in 2006 and many organizations such as IEEE Fellow Committee (2011-2012); IEEE Computational Intelligence Society Awards Committee (2008, 2012, 2014), IEEE Systems, Man, and Cybernetics Society Board of Directors (2013-2015), He is an IEEE Fellow, IAPR Fellow, and a recipient of an IEEE Transactions on Neural Networks Outstanding Paper Award and APNNA Outstanding Achievement Award in 2011, Natural Science Awards from Shanghai Municipal Government (2009) and Ministry of Education of China (2011), and Neural Networks Pioneer Award from IEEE Computational Intelligence Society (2014), among others.

We are Champions!

We are pleased to announce that we are champions of IEEE the Ghosts Challenge’ 2013!  http://cis.ieee.org/index.php?option=com_content&view=article&id=320:the-outcome-of-the-ieee-cis-ghosts&catid=17:e-newsletter-news-a-announcements .

This game competition was organized by the IEEE CIS Student Games-based Competition Committee and our chapter sent the BLISS team to enter the competition (https://ghosts-challenge.math.unipd.it).  HUANG Zhongqian (leader of the team) and CAI Yueliang are members of the team. Dr. Min JIANG is the supervisor.

In the competition, the BLISS combined Monte – Carlo Search Tree and Standard model technique to settle the problem. If you are interested in our source code, please contact us.