%0 Journal Article
%T Learning Identification: Least Squares Algorithms and Their Repetitive Consistency<br>学习辨识:最小二乘算法及其重复一致性
%A SUN Ming-Xuan
%A BI Hong-Bo
%A <br>孙明轩
%A 毕宏博
%J 自动化学报
%D 2012
%I 
%X This paper presents a learning identification method for stochastic systems with time-varying parametric uncertainties. The systems undertaken perform tasks repetitively over a pre-specified finite-time interval, and a least squares learning algorithm is derived on the basis of the repetitive operations. The learning identification method applies to periodically time-varying systems. It is shown that the estimates converge to the time-varying values of the parameters, and the complete estimation can be achieved under repetitive persistent excitation condition, a sufficient condition for establishing repetitive consistency of the learning algorithms. Numerical results are presented to demonstrate the effectiveness of the proposed learning algorithms.
%K Learning identification
%K least squares
%K stochastic time-varying systems
%K repetitive consistency<br>学习辨识
%K 最小二乘法
%K 随机时变系统
%K 重复一致性
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=E76622685B64B2AA896A7F777B64EB3A&aid=1393146D229514ED8A8443389C4CBEF8&yid=99E9153A83D4CB11&vid=16D8618C6164A3ED&iid=94C357A881DFC066&sid=6CDD207A90CE1EEC&eid=C19D5524C51D7FE4&journal_id=0254-4156&journal_name=自动化学报&referenced_num=0&reference_num=19