Gradient based and least-squares based iterative identification methods for OE and OEMA systems ?
Gradient based and least-squares based iterative identification algorithms are developed for output error (OE) and output error moving average (OEMA) systems. Compared with recursive approaches, the proposed iterative algorithms use all the measured inputoutput data at each iterative computation (at each iteration), and thus can produce highly accurate parameter estimation. The basic idea of the iterative methods is to adopt the interactive estimation theory: the parameter estimates relying on unknown variables are computed by using the estimates of these unknown variables which are obtained from the preceding parameter estimates. The simulation results confirm theoretical findings.
iterative algorithms parameter estimation recursive identification least squares stochastic gradient missing data output error (oe) models output error moving average (oema) models