Open AccessTwo‐stage parameter estimation algorithms for Box–Jenkins systems
Author(s) -
Ding Feng,
Duan Honghong
Publication year - 2013
Publication title -
iet signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.384
H-Index - 42
eISSN - 1751-9683
pISSN - 1751-9675
DOI - 10.1049/iet-spr.2012.0183
Subject(s) - key (lock) , algorithm , computer science , estimation theory , noise (video) , box–jenkins , stage (stratigraphy) , least squares function approximation , system identification , identification (biology) , recursive least squares filter , computational complexity theory , mathematics , artificial intelligence , time series , data modeling , statistics , machine learning , adaptive filter , autoregressive integrated moving average , botany , computer security , database , estimator , image (mathematics) , biology , paleontology
A two‐stage recursive least‐squares identification method and a two‐stage multi‐innovation stochastic gradient method are derived for Box–Jenkins (BJ) systems. The key is to decompose a BJ system into two subsystems, one containing the parameters of the system model and the other containing the parameters of the noise model, and then to estimate the parameters of the system model and the noise model, respectively. The simulation examples indicate that the proposed algorithms can generate highly accurate parameter estimates and require small computational burden.