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

發布者:數學與信息學院發布時間:2019-07-03浏覽次數:1

报告人:陈阳泉教授 加州大学
時間:2019年7月30日下午16:00-17:00               
地點:數學系713室


報告摘要: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.


報告人簡介:陈阳泉教授,博士生導師,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.).


歡迎廣大師生參加!


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