Model Quality in Recursive Identification of Time Varying Systems with ARX Structure

Recursive identification of time varying systems with ARX structures is considered, with the interest focused on the quality of the estimated transfer functions. Three recursive identification algorithms are considered, the least mean squares (LMS) algorithm, the recursive least squares (RLS) algorithm and the Kalman filter respectively. The model quality is studied in terms of algorithm design variables, input and disturbance signal properties and variations of the true system. Using asymptotic methods approximate expressions for the model quality are derived.