Title :
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
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;
Conference_Titel :
Decision and Control, 1996., Proceedings of the 35th IEEE Conference on
Conference_Location :
Kobe
Print_ISBN :
0-7803-3590-2
DOI :
10.1109/CDC.1996.573483
