报告题目： The State of the of Art of Neurodynamic Optimization – Past, Present and Future
报 告 人： Jun Wang （王钧）（IEEE Fellow, IAPR Fellow）
报告时间： 2013年12月31号10：30
报告地点： 南一楼中311会议室
邀 请 方：“多谱信息处理技术”国家级重点实验室
报告摘要：
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 brain is a profound dynamic system and its neurons are always active from their birth to death. When a decision is to be made in the brain, many of its neurons are highly activated to gather information, search memory, compare differences, and make inference and decision. Recurrent neural networks are brain-like nonlinear dynamic system models and can be properly designed to imitate biological counterparts and serve as goal-seeking parallel computational models for solving optimization problems in a variety of settings. Neurodynamic optimization can be realized physically in designated hardware such as application-specific integrated circuits (ASICs) where optimization is carried out in a parallel and distributed manner, where the convergence rate of the optimization process is independent of the problem dimensionality. Because of the inherent nature of parallel and distributed information processing, neurodynamic optimization can handle large-scale problems. In addition, neurodynamic optimization may be used for optimizing dynamic systems in multiple time-scales with parameter-controlled convergence rate.
These salient features are particularly desirable for dynamic optimization in decentralized decision-making scenarios. While population-based evolutionary approaches to optimization emerged as prevailing heuristic and stochastic methods in recent years, neurodynamic optimization deserves great attention in its own rights due to its close ties with optimization and dynamical systems theories, as well as its biological plausibility and circuit implementability with VLSI or optical technologies.
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 the following dimensions:
(i) Solution quality: Designed based on smooth penalty methods with finite penalty parameter, the earliest neurodynamic optimization models can converge to approximate solutions only. Later on, our models designed based on other design principles can guarantee to state or output convergence to exact optima of solvable optimization problems.
(ii) Solvability scope: The solvability scope of our neurodynamic optimization has been expanded from linear programming problems, to quadratic programming, to smooth convex programming problems with various constraints, to nonsmooth convex optimization problems, recently to nonsmooth optimization with generalized convex objective functions or constraints.
(iii) Convergence property: The convergence property of our neurodynamic optimization models has been extended from near-optimum, to conditional exact-optimum global convergence, to guaranteed global convergence, to faster global exponential convergence to even more desirable finite-time convergence, with increasing convergence rate.
(iv) Model complexity: The neurodynamic optimization models for constrained optimization are essentially of multi-layer due to the introduction of instrumental variables for constraint handling (e.g., Lagrange multipliers or dual variables). The architectures of our recent neurodynamic optimization models for solving linearly constrained optimization problems have been reduced from multi-layer structures to single-layer ones with decreasing model complexity to facilitate their implementation.
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.
报告人简历：
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 160 journal papers, 13 book chapters, 8 edited books, and numerous conference papers in these areas. He has been an Associate Editor of the IEEE Transactions on Cybernetics (and its predecessor) since 2003 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) 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, as a guest editor of special issues of European Journal of Operational Research (1996), International Journal of Neural Systems (2007), Neurocomputing (2008), 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. He was an IEEE Computational Intelligence Society Distinguished Lecturer (2010-2012). 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), 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 (2012), among others.
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