An approach to the generation of stopping rules in parametric identification problems is proposed on the basis of the computation of a statistic of the difference between two successive estimates. This statistic is also used for fault detection in the Kalman filter. The developed decision rules are applied to a linear system identification problem. Experimental results are presented to demonstrate the performance of the proposed algorithms.
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