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On the choice of noise models and their bounds in set-membership identification
Conference proceeding

On the choice of noise models and their bounds in set-membership identification

Er-Wei Bai, Hyonyong Cho and R Tempo
PROCEEDINGS OF THE 35TH IEEE CONFERENCE ON DECISION AND CONTROL, VOLS 1-4, pp.2412-2417
1996
DOI: 10.1109/CDC.1996.573450

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Abstract

Different noise models and the corresponding membership sets are studied in this paper. In particular, under some conditions on the noise sequences, we show that: (1) If the noise bound is unknown and tight, then the size of the membership sets converges to zero asymptotically and (2) If the noise bound is unknown but tight, then the estimated noise bound calculated from the observed input-output data converges to the true but unknown noise bound asymptotically.

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