RECURSIVE SYSTEM IDENTIFICATION  

作　　者：陈翰馥 

机构地区：[1]Key Laboratory of Systems and Control, Institute of Systems Science, AMSS Chinese Academy of Sciences,Beijing 100190, China

出　　处：《Acta Mathematica Scientia》2009年第3期650-672,共23页数学物理学报（B辑英文版）

基　　金：supported by NSFC (60221301 and 60874001);a grant from the National Laboratory of Space Intelligent Control

摘　　要：Most of existing methods in system identification with possible exception of those for linear systems are off-line in nature, and hence are nonrecursive. This paper demonstrates the recent progress in recursive system identification. The recursive identification algorithms are presented not only for linear systems （multivariate ARMAX systems） but also for nonlinear systems such as the Hammerstein and Wiener systems, and the nonlinear ARX systems. The estimates generated by the algorithms are online updated and converge a.s. to the true values as time tends to infinity.Most of existing methods in system identification with possible exception of those for linear systems are off-line in nature, and hence are nonrecursive. This paper demonstrates the recent progress in recursive system identification. The recursive identification algorithms are presented not only for linear systems （multivariate ARMAX systems） but also for nonlinear systems such as the Hammerstein and Wiener systems, and the nonlinear ARX systems. The estimates generated by the algorithms are online updated and converge a.s. to the true values as time tends to infinity.

关 键 词：recursive identification ARMAX Hammerstein systems Wiener systems nonlinear ARX systems stochastic approximation CONVERGENCE 

分 类 号：N945.14[自然科学总论—系统科学]