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吴争光,博士,特聘讲座教授

作者:    信息来源:    发布时间: 2020-09-21

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吴争光,博士,特聘讲座教授;

浙江大学长聘教授,博士生导师,在2019年入选国家“万人计划”青年拔尖人才。主要开展网络化系统、信息物理系统与智能电网的研究工作。目前主持国家自然科学基金重点项目,面上项目和青年基金项目各一项,浙江省杰出青年科学基金。2014-2019年连续六年入选Elsevier中国高被引学者榜单,2017-2020年连续四年入选Clarivate Analytics全球高被引科学家榜单。

曾获得浙江省2011年优秀博士研究生学位论文奖、2015年教育部自然科学一等奖、2017年浙江省自然科学二等奖,2019 年中国自动化学会自然科学一等奖和第三届中国自动化学会二等教育教学成果等奖,在IEEE系列汇刊和Automatica上发表(含录用)论文120余篇,2篇论文分别入选2013和2014年中国百篇最具影响国际学术论文,在Springer出版社出版英文专著4部。论文被SCI他引4000多次,共有40篇论文入选ESI高被引论文,h指数50。IEEE CSS会议编委会Associate Editor,以及多个国际会议的程序委员会主席/成员。

代表性论文

(1).Zheng-Guang Wu, Peng Shi, Zhan Shu, Hongye Su, and Renquan Lu,Passivity-based asynchronous control for Markov jump systems,IEEE Transactions on Automatic Control,vol. 62, no. 4, pp. 2020-2025,2017.

(2).Zheng-Guang Wu, Peng Shi, Hongye Su, and Renquan Lu,Dissipativity-basedsampled-datafuzzycontroldesignanditsapplicationtotruck-trailersystem,IEEE Transactions on Fuzzy Systems, vol. 23, no. 5, pp. 1669-1679,2015. (Regular Paper)

(3).Zheng-Guang Wu, Peng Shi, Hongye Su, and Jian Chu,Local synchronization of chaotic neural networks with sampled-data and saturating actuators,IEEE Transactions on Cybernetics,vol. 44, no. 12, pp. 2635-2645,2014. (Regular Paper)

(4).Zheng-Guang Wu, Peng Shi, Hongye Su, and Jian Chu,Exponential stabilization for sampled-data neural-network-based control systems,IEEE Transactions on Neural Networks and Learning Systems,vol. 25, no. 12, pp. 2180-2190,2014.(Regular Paper)

(5).Zheng-Guang Wu, Peng Shi, Hongye Su, and Jian Chu,Sampled-data fuzzy control of chaotic systems based on T-S fuzzy model,IEEE Transactions on Fuzzy Systems, vol. 22, no. 1, pp. 153-163,2014.(Regular Paper)

(6).Zheng-Guang Wu, Peng Shi, Hongye Su, and Jian Chu,Network-based robust passive control for fuzzy systems with randomly occurring uncertainties,IEEE Transactions on Fuzzy Systems, vol. 21, no. 5, pp. 966-971,2013.

(7).Zheng-Guang Wu, Peng Shi, Hongye Su, and Jian Chu,Dissipativity analysis for discrete-time stochastic neural networks with time-varying delays,IEEE Transactions on Neural Networks and Learning Systems, vol. 24, no. 3, pp. 345-355,2013.(Regular Paper) [1].

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