When the problem of restricted complexity identification is addressed in a set membership setting, the selection of the worst-case optimal model requires the solution of complex optimization problems. This paper studies different classes of suboptimal estimators and provides tight upper bounds on their identification error, in order to assess the reliability level of the identified models. Results are derived for fairly general classes of sets and norms, in the framework of Information Based Complexity theory.

Garulli, A., B., K., Vicino, A., & G., Z. (1999). Suboptimal conditional estimators for restricted complexity set membership identification. In ROBUSTNESS IN IDENTIFICATION AND CONTROL (pp. 117-133). London : Springer Verlag.

Suboptimal conditional estimators for restricted complexity set membership identification

GARULLI, ANDREA;VICINO, ANTONIO;
1999

Abstract

When the problem of restricted complexity identification is addressed in a set membership setting, the selection of the worst-case optimal model requires the solution of complex optimization problems. This paper studies different classes of suboptimal estimators and provides tight upper bounds on their identification error, in order to assess the reliability level of the identified models. Results are derived for fairly general classes of sets and norms, in the framework of Information Based Complexity theory.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11365/29429
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