Abstract
Abstract Recursive identification of time varying systems using ARX models is considered, with focus on the accuracy of the transfer function estimates. Three recursive identification algorithms are studied, the least mean squares algorithm, the recursive least squares algorithm and the Kalman filter respectively. The model accuracy 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. The derived expressions are validated by simulations.
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