Distributed Least Squares Algorithm of Continuous-Time Stochastic Regression Model Based on Sampled Data  

作　　者：ZHU Xinghua GAN Die LIU Zhixin 

机构地区：[1]The Key Laboratory of Systems and Control,Academy of Mathematics and Systems Science,Chinese Academy of Sciences,and School of Mathematical Sciences,University of Chinese Academy of Sciences,Beijing 100190,China [2]Zhongguancun Laboratory,Beijing 100094,China

出　　处：《Journal of Systems Science & Complexity》2024年第2期609-628,共20页系统科学与复杂性学报（英文版）

基　　金：supported by the Natural Science Foundation of China under Grant No.T2293772;the National Key R&D Program of China under Grant No.2018YFA0703800;the Strategic Priority Research Program of Chinese Academy of Sciences under Grant No.XDA27000000;the National Science Foundation of Shandong Province under Grant No.ZR2020ZD26.

摘　　要：In this paper,the authors consider the distributed adaptive identification problem over sensor networks using sampled data,where the dynamics of each sensor is described by a stochastic differential equation.By minimizing a local objective function at sampling time instants,the authors propose an online distributed least squares algorithm based on sampled data.A cooperative non-persistent excitation condition is introduced,under which the convergence results of the proposed algorithm are established by properly choosing the sampling time interval.The upper bound on the accumulative regret of the adaptive predictor can also be provided.Finally,the authors demonstrate the cooperative effect of multiple sensors in the estimation of unknown parameters by computer simulations.

关 键 词：Cooperative excitation condition distributed least squares REGRET sampled data stochastic differential equation 

分 类 号：TP212.9[自动化与计算机技术—检测技术与自动化装置] TN929.5[自动化与计算机技术—控制科学与工程]