Sparse input matrix and state estimation for linear systems

Abstract : This paper addresses the problem of sparse identification of the input matrix parameters in linear systems. A filter that combines state and sparse input matrix estimation is developed. This takes advantage of the connections between Kalman filtering and least squares estimation to formulate the problem as a ℓ1 regularised least squares optimisation, i.e. as a LASSO problem. The solution consistency is discussed and the technique is applied to experimental measurements from a production web server with promising results.
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Communication dans un congrès
Decision and Control (CDC), 2010 49th IEEE Conference on, Dec 2010, United States. pp.4441-4446, 2010, <10.1109/CDC.2010.5718016>
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https://hal.archives-ouvertes.fr/hal-00616734
Contributeur : Laetitia Chapel <>
Soumis le : mercredi 24 août 2011 - 10:31:22
Dernière modification le : lundi 21 mars 2016 - 17:38:57

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Laetitia Chapel, Douglas J. Leith. Sparse input matrix and state estimation for linear systems. Decision and Control (CDC), 2010 49th IEEE Conference on, Dec 2010, United States. pp.4441-4446, 2010, <10.1109/CDC.2010.5718016>. <hal-00616734>

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