System identification in the presence of unmodeled dynamics-a principal components extraction approach

Author

Tsin, Yanghai ; Li, Yaotong

Author_Institution

Inst. of Autom., Acad. Sinica, Beijing, China

Volume

3

fYear

1996

fDate

11-13 Dec 1996

Firstpage

2555

Abstract

In this paper a two-step method for identification is presented. The first step is to identify FIR sequences using any existing efficient algorithm. The second step is principal components extraction. It tries to recover the complete system performance from the FIR sequences estimated. It is shown that the denominator parameter of the obtained ARMAX model is the eigenvector corresponding to the eigenvalue of a certain matrix composed of the estimated FIR sequence. The eigenvalue itself can be an index of model order selection. A criterion for selecting the FIR sequence length is presented. Simulation result demonstrates the effectiveness of the approach

Keywords

autoregressive moving average processes; eigenvalues and eigenfunctions; estimation theory; identification; ARMAX model; FIR sequences; complete system performance; eigenvalue; eigenvector; model order selection index; principal components extraction approach; system identification; two-step method; unmodeled dynamics; Automatic control; Automation; Control systems; Data mining; Eigenvalues and eigenfunctions; Finite impulse response filter; Laboratories; System identification; System performance; Tail;

fLanguage

English

Publisher

ieee

Conference_Titel

Decision and Control, 1996., Proceedings of the 35th IEEE Conference on

Conference_Location

Kobe

ISSN

0191-2216

Print_ISBN

0-7803-3590-2

Type

conf

DOI

10.1109/CDC.1996.573483

Filename

573483