The Advances in Collaborative Neurodynamic Optimization 同题参见
主 讲 人 ：Jun Wang 教授
活动时间：08月21日10时00分
地 点 ：数学科学开云手机网官方网站D204室
讲座内容：
The past four decades have witnessed the emergence and growth of neurodynamic optimization, which has become a potentially powerful tool for solving constrained optimization problems. This talk will present advances in neurodynamic optimization. Specifically, in the proposed collaborative neurodynamic optimization framework, multiple neurodynamic optimization models with different initial states are employed for scattered searches. In addition, a meta-heuristic rule in swarm intelligence (e.g., PSO) is used to reposition the search of neurons upon local convergence, thereby escaping local minima and moving toward global optima. Problem formulations and experimental results will be highlighted to substantiate the viability and efficacy of several specific paradigms in this framework for supervised/semi-supervised feature selection, supervised learning, compressive sensing, and model predictive control.
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主讲人介绍：
Jun Wang is a Chair Professor of Computational Intelligence in the Departments of Computer Science and Data Science at City University of Hong Kong. Prior to this position, he held various academic positions at Dalian University of Technology, Case Western Reserve University, University of North Dakota, and the Chinese University of Hong Kong. He also held various short-term visiting positions at the USAF Armstrong Laboratory, the RIKEN Brain Science Institute, and the Shanghai Jiao Tong University. He received a B.S. and an M.S. from Dalian University of Technology, and a Ph.D. from Case Western Reserve University. He is the Editor-in-Chief-Elect of the IEEE Transactions on Artificial Intelligence and a Past Editor-in-Chief of the IEEE Transactions on Cybernetics. He is an IEEE Life Fellow, IAPR Fellow, HKAE Fellow, and a foreign member of Academia Europaea. He is a recipient of the APNNA Outstanding Achievement Award, IEEE CIS Neural Networks Pioneer Award, CAAI Wu Wenjun AI Achievement Award, and IEEE SMCS Norbert Wiener Award, among other distinctions.
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