TY - JOUR
T1 - Auxiliary model based recursive generalized least squares identification algorithm for multivariate output-error autoregressive systems using the decomposition technique
AU - Liu, Qinyao
AU - Ding, Feng
AU - Wang, Yan
AU - Wang, Cheng
AU - Hayat, Tasawar
N1 - Publisher Copyright:
© 2018 The Franklin Institute
PY - 2018/10
Y1 - 2018/10
N2 - This paper focuses on the parameter estimation problem of multivariate output-error autoregressive systems. Based on the decomposition technique and the auxiliary model identification idea, we derive a decomposition based auxiliary model recursive generalized least squares algorithm. The key is to divide the system into two fictitious subsystems, the one including a parameter vector and the other including a parameter matrix, and to estimate the two subsystems using the recursive least squares method, respectively. Compared with the auxiliary model based recursive generalized least squares algorithm, the proposed algorithm has less computational burden. Finally, an illustrative example is provided to verify the effectiveness of the proposed algorithms.
AB - This paper focuses on the parameter estimation problem of multivariate output-error autoregressive systems. Based on the decomposition technique and the auxiliary model identification idea, we derive a decomposition based auxiliary model recursive generalized least squares algorithm. The key is to divide the system into two fictitious subsystems, the one including a parameter vector and the other including a parameter matrix, and to estimate the two subsystems using the recursive least squares method, respectively. Compared with the auxiliary model based recursive generalized least squares algorithm, the proposed algorithm has less computational burden. Finally, an illustrative example is provided to verify the effectiveness of the proposed algorithms.
UR - http://www.scopus.com/inward/record.url?scp=85052729210&partnerID=8YFLogxK
U2 - 10.1016/j.jfranklin.2018.07.043
DO - 10.1016/j.jfranklin.2018.07.043
M3 - Article
AN - SCOPUS:85052729210
SN - 0016-0032
VL - 355
SP - 7643
EP - 7663
JO - Journal of the Franklin Institute
JF - Journal of the Franklin Institute
IS - 15
ER -