报告题目：On two non-asymptotic estimation methods
报告人：刘大研Associate Professor/法国卢瓦尔河谷国立应用科学学院
报告时间： 2019年8月10日星期六15:00
报告地点：电气楼304
报告对象：电气、数理、计算机及相关学院研究生及感兴趣教师
主办单位：beat365
报告人简介：
刘大研，任职法国中部大区PRISME实验室控制组，法国中部卢瓦尔河谷国立应用科学学院副教授，燕山大学客座教授，博导。于2011年获法国里尔一大应用数学博士学位，在法国国立高等工程技术学校和沙特阿拉伯国王阿卜杜拉科技大学完成博士后工作后。刘博士的主要研究兴趣在于整数阶和分数阶系统的辨识和估计。在国际期刊和会议上发表了60多篇论文，例如IEEE Transactions on Automatic Control， Automatica，SIAM Journal of Scientific Computing， Systems & Control Letters等。 曾获中国政府颁发的海外优秀自费职工奖，Applied Mathematics and Computation、Journal of The Franklin Institute和 Neurocomputing杂志优秀审稿人。2017年任国际自动控制联盟《线性控制系统》技术委员会成员， 2019年任中国自动化学会分数阶系统与控制专业委员会委员。自2019年5月起，他被任命为Fractal and Fractional杂志编委委员。
On two non-asymptotic estimation methods
For cost and technological reasons, there always exist some variables and parameters which cannot be measured. Moreover, the measurements usually contain noises. Sometime, fast estimations with convergence in finite-time are required in on-line applications. For these reasons, the modulating functions method introduced by Shinbrot in 1954 and the algebraic parametric estimation method introduced by Fliess and Sira-Ramirez in 2003 both originally for system identification have been applied and extended in signal processing and automatic control, such as parameter estimation and numerical differentiation, etc. The two methods have the following advantages. Firstly, the obtained estimators are exactly given by integral formulae of the observation signal. Thus, they are algebraic and non-asymptotic. Fast estimation can be provided using sliding integration window with finite length. The knowledge of initial conditions is not needed and the derivatives of noisy signals don't need to be calculated. Moreover, thanks to the integrals in the formulae, they are robust with respect to corrupting noises without the need of knowing in priori their statistical properties. Recently, these methods have been extended to fractional order systems. In this talk, the ideas of these two methods will be explained by giving simples examples.Moreover, the applications to parameter estimation and numerical differentiation will be presented.
