This paper addresses system identification using binary-valued sensors in a worst-case setting. The first contribution is an upper bound on time complexity for identification of FIR models, which improves over existing bounds in the literature. The second result concerns the solution of the optimal input design problem for identification of a scalar gain. It is shown that the two contributions can be combined to construct suboptimal input signals for identification of FIR models of arbitrary order.

Casini, M., Garulli, A., & Vicino, A. (2007). Time complexity and input design in worst-case identification using binary sensors. In Proceedings of the 46th IEEE Conference on Decision and Control (pp.5528-5533) [10.1109/CDC.2007.4434445].

Time complexity and input design in worst-case identification using binary sensors

CASINI, MARCO;GARULLI, ANDREA;VICINO, ANTONIO
2007

Abstract

This paper addresses system identification using binary-valued sensors in a worst-case setting. The first contribution is an upper bound on time complexity for identification of FIR models, which improves over existing bounds in the literature. The second result concerns the solution of the optimal input design problem for identification of a scalar gain. It is shown that the two contributions can be combined to construct suboptimal input signals for identification of FIR models of arbitrary order.
Casini, M., Garulli, A., & Vicino, A. (2007). Time complexity and input design in worst-case identification using binary sensors. In Proceedings of the 46th IEEE Conference on Decision and Control (pp.5528-5533) [10.1109/CDC.2007.4434445].
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11365/21792
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