%0 Journal Article
%T Recursive Subspace Identification Based on Principal ComponentAnalysis<br>基于主成份分析的递推子空间辨识
%A Jiang Yueping
%A Fang Haitao
%A <br>姜月萍
%A 方海涛
%J 系统科学与数学
%D 2007
%I 
%X The recursive subspace identification problem of MIMO state space models is considered. In the case of the existence of output-measurement noise, a new recursive algorithm based on SA-PCA (Stochastic Approximation-Principal Component Analysis) is proposed to estimate a basis of the extended observability matrix. Besides, a recursive algorithm based on RLS (Recursive Least-Squares) is proposed to estimate the system matrices. Finally, a numerical simulation is given to show the validity of the algorithm.
%K State-space models
%K recursive subspace identification
%K principal component analysis
%K recursive least squares<br>状态空间模型
%K 递推子空间辨识
%K 主成份分析
%K 递推最小二乘
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=6E709DC38FA1D09A4B578DD0906875B5B44D4D294832BB8E&cid=37F46C35E03B4B86&jid=0CD45CC5E994895A7F41A783D4235EC2&aid=C78B754ECF6307B8&yid=A732AF04DDA03BB3&vid=DB817633AA4F79B9&iid=38B194292C032A66&sid=117BC32987199759&eid=8ACD9060100C26F1&journal_id=1000-0577&journal_name=系统科学与数学&referenced_num=0&reference_num=18