黄廷文教授报告

发布时间:2018年07月23日 作者:杨东辉   消息来源:    阅读次数:[]

报告题目: Efficient computational approaches and their applications

报告人: 黄廷文教授

报告时间: 2018年07月24日下午3:00-5:00

报告地点: 数学院 145 报告厅

报告摘要: In this talk, efficient computational approaches to game theoretic model, large scale optimization problem will be introduced to solving different challenging problems. In a smart grid context, a demand response strategy of electric vehicle charging is modelled by a stochastic game, where a big data analytic framework is proposed for controlling the electric vehicle charging behaviours. Moreover, a two-stage stochastic game theoretical model is proposed for energy trading problem in a multi-energy microgrid system. In these two work, the risk measurement technique, conditional value at risk(CVaR), is harnessed to estimate the overload risk during the peak hour and the overbidding risk while distributed alternating direction method of multipliers (ADMM) is accelerated by Nesterov gradient method to solve two game models. Concerning the privacy, a research branch of reinforcement learning (RL) that dominates distributed learning for years will be presented by making the first attempt to apply RL-based algorithms in the energy trading game among smart microgrids where no information concerning the distribution of payoffs is a priori available and the strategy chosen by each microgrid is private to opponents, even trading partners. To solve this challenge, a new energy trading framework based on the repeated game that enables each microgrid to individually and randomly choose a strategy with probability to trade the energy in an independent market so as to maximize the average revenue. In addition, for a large scale economic dispatch problem, different distributed optimization algorithms are developed, including a fast event-triggered scheme and consensus based multiagent methods.

报告人简介: Tingwen Huang (黄廷文) is a professor at Texas A&M University-Qatar. He received his B.S. degree from Southwest Normal University (now Southwest University), China, 1990, his M.S. degree from Sichuan University, China, 1993, and his Ph.D. degree from Texas A&M University, College Station, Texas, USA, 2002. After graduated from Texas A&M University, he worked as a Visiting Assistant Professor there. Then he joined Texas A&M University at Qatar (TAMUQ) as an Assistant Professor in 2003, then he was promoted to Professor in 2013. His research interests include neural networks based computational intelligence, distributed control and optimization, nonlinear dynamics and applications in smart grids. He has published more than three hundreds peer-review reputable SCI journal papers. One of this projects funded by Qatar National Research Fund, was awarded the Best Research Project by Qatar National Research Fund in 2015. He was awarded the Faculty Research Excellence Award by TAMUQ in 2015. Moreover, he is also active in professional services. In 2012, he served as the President of Asia Pacific Neural Network Assembly (renamed to Asia Pacific Neural Network Society now). Currently, he serves as an associate editor for four journals including IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Cybernetics, and Cognitive Computation. As the General Chair, he organized the 14th International Workshop on Complex Systems and Networks (IWCSN2017), the 9th International Conference on Advanced Computational Intelligence (ICACI2017), the 19th International Conference on Neural Information Processing (ICONIP2012).



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