学术报告:Optimal way to optimize using optimized randomness and its connection to fractional calculus

编辑: 陈亮君    发布时间:2019-07-03    次点击

报告人:YangQuan Chen (陈阳泉) 教授,博导(加州大学,默塞德分校)

时间:2019年7月30日下午16:00-17:00                

地点:数学系713室

联系人:袁利国 


报告摘要:In this talk, first I will show 1) in swarm based search (such as PSO – particle swarm optimization)  using randomness with different heavy-tailed distributions, the search performance can be further optimized beyond Levy; 2)  the connection of heavytailedness and fractional calculus. It is hoped that this talk will open new investigations in new optimal ways to optimize using optimized randomness with the help of fractional calculus in this bigdata and machine learning era. 


报告人简介:陈阳泉教授,博士生导师,现任职于美国加州大学默塞德分校工程学院,主要研究领域为分数阶微积分理论及应用,分布式测量及基于移动执行器传感器网络的分布式参数系统的分布式控制,复杂信号的分数阶信号处理理论及应用,智慧机电一体化与控制,小型无人机多谱遥感及精准农业应用等。陈教授是国际刊物IFAC Mechatronics, Nonlinear Dynamics, FCAA (Fractional Calculus and Applied Analysis); Springer Journal of Intelligent & Robotic Systems; 和 Springer Intelligent Service Robotics的副主编. International Journal of Advanced Robotic Systems (IJARS) 的田野机器人领域主编. 陈教授曾是国际刊物IFAC Control Engineering Practice; IEEE Transactions on Control Systems Technology; IET Control Theory and Applications; ISA Transactions,ASME J. of Dynamic Systems, Measurement and Control的副主编。发表论文数百篇,美国专利十几个,研究专著和教科书近20部,其中ESI 高备引论文10余篇,SCI收录250余篇,Publon引用12500余次 (H-index 56), Google学术搜索引用超过三万次(H-index 79). 他是2018全球高被引学者之一(Clarivate Analytics Inc.).


欢迎广大师生参加!


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