On iterative parameter estimation algorithms for OE and OEMA systems

Two iterative identification algorithms, a gradient and a least-squares based, are developed for output error (OE) and output error moving average (OEMA) systems. Compared with recursive approaches, these two off-line iterative algorithms use all the measured input-output data at each iterative computation (at each iteration), and thus can produce highly accurate parameter estimation. The basic idea of the iterative methods proposed 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.