IEEE Fellow, CIS Distinguished Lecturer
Chinese Univ. of Hong Kong, China
Talk 1: The State of the Art of Neurodynamic Optimization – Past, Present, and Prospect.
Abstract: Optimization is omnipresent in nature and society, and an important tool for problem-solving in science, engineering, and commerce. Optimization problems arise in a wide variety of applications such as the design, planning, control, operation, and management of engineering systems. In many applications (e.g., online pattern recognition and in-chip signal processing in mobile devices), real-time optimization is necessary or desirable.
For such applications, conventional optimization techniques may not be competent due to stringent requirement on computational time. It is computationally challenging when optimization procedures have to be performed in real time to optimize the performance of dynamical systems.
The past three decades witnessed the birth and growth of neurodynamic optimization. Although a couple of circuit-based optimization methods were developed in earlier, it was perhaps Hopfield and Tank who spearheaded the neurodynamic optimization research in the context of neural computation with their seminal works in mid-1980’s. Tank and Hopfield extended the continuous-time Hopfield network for linear programming. Kennedy and Chua developed a neural network for nonlinear programming. It is proven that the state of the neurodynamics is globally convergent and an equilibrium corresponding to an approximate optimal solution of the given optimization problems. Over the years, the neurodynamic optimization research has made significant progresses with numerous models with improved features for solving various optimization problems. Substantial improvements of neurodynamic optimization theory and models have been made in several dimensions.
In this talk, starting with the idea and motivation of neurodynamic optimization, we will review the historic review and present the state of the art of neurodynamic optimization with many models and selected applications. Theoretical results about the state stability, output convergence, and solution optimality of the neurodynamic optimization models will be given along with many illustrative examples and simulation results. Four classes of neurodynamic optimization model design methodologies (i.e., penalty methods, Lagrange methods, duality methods, and optimality methods) will be delineated with discussions of their characteristics. In addition, it will be shown that many real-time computational optimization problems in information processing, system control, and robotics (e.g., parallel data selection and sorting, robust pole assignment in linear feedback control systems, robust model predictive control for nonlinear systems, collision-free motion planning and control of kinematically redundant robot manipulators with or without torque optimization, and grasping force optimization of multi-fingered robotic hands) can be solved by means of neurodynamic optimization. Finally, prospective future research directions will be discussed.
Talk 2: Neurodynamic Optimization Approaches to Parallel Data Selection in the Era of Big Data.
Abstract: In the present information era, huge amount of data to be processed daily. In contrast of conventional sequential data processing techniques, parallel data processing approaches can expedite the processes and more efficiently deal with big data. In the last few decades, neural computation emerged as a popular area for parallel and distributed data processing. The data processing applications of neural computation included, but not limited to, data sorting, data selection, data mining, data fusion, and data reconciliation. In this talk, neurodynamic approaches to parallel data processing will be introduced, reviewed, and compared. In particular, my talk will compare several mathematical problem formulations of well-known multiple winners-take-all problem and present several recurrent neural networks with reducing model complexity. Finally, the best one with the simplest model complexity and maximum computational efficiency will be highlighted. Analytical and Monte Carlo simulation results will be shown to demonstrate the computing characteristics and performance of the continuous-time and discrete-time models. The applications to parallel sorting, rank-order filtering, and data retrieval will be also discussed.
Biography: Jun Wang is a Professor and the Director of the Computational Intelligence Laboratory in the Department of Mechanical and Automation Engineering at the Chinese University of Hong Kong. Prior to this position, he held various academic positions at Dalian University of Technology, Case Western Reserve University, and University of North Dakota. He also held various short-term visiting positions at USAF Armstrong Laboratory (1995), RIKEN Brain Science Institute (2001), Universite Catholique de Louvain (2001), Chinese Academy of Sciences (2002), Huazhong University of Science and Technology (2006–2007), and Shanghai Jiao Tong University (2008-2011) as a Changjiang Chair Professor. Since 2011, he is a National Thousand-Talent Chair Professor at Dalian University of Technology on a part-time basis. He received a B.S. degree in electrical engineering and an M.S. degree in systems engineering from Dalian University of Technology, Dalian, China. He received his Ph.D. degree in systems engineering from Case Western Reserve University, Cleveland, Ohio, USA. His current research interests include neural networks and their applications. 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 on Cybernetics since 2014 and a member of the editorial board of Neural Networks since 2012. He also served as an Associate Editor of the IEEE Transactions on Neural Networks (1999-2009), IEEE Transactions on Cybernetics and its predecessor (2003-2013), and IEEE Transactions on Systems, Man, and Cybernetics – Part C (2002–2005), as a member of the editorial advisory board of International Journal of Neural Systems (2006-2013), as a guest editor of special issues of European Journal of Operational Research (1996), International Journal of Neural Systems (2007), Neurocomputing (2008, 2014), and International Journal of Fuzzy Systems (2010, 2011). He was an organizer of several international conferences such as the General Chair of the 13th International Conference on Neural Information Processing (2006) and the 2008 IEEE World Congress on Computational Intelligence, and a Program Chair of the IEEE International Conference on Systems, Man, and Cybernetics (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.