Recursive Subspace Identification Based on Principal ComponentAnalysis
基于主成份分析的递推子空间辨识

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.