Undermodelling Detection with Sign-Perturbed Sums * *The work of A. Carè was supported by the European Re-search Consortium for Informatics and Mathematics (ERCIM) and the Australian Research Council (ARC) under Discovery Grant DP130104028. The work of M.C. Campi was partly supported by MIUR - Ministero dell’Istruzione, dell’Università e della Ricerca and by the H & W program of the University of Brescia under the project CLAFITE. The work of B. Cs. Csáji was supported by the GINOP-2.3.2-15-2016-00002 grant and by the János Bolyai Research Fellowship, BO/00217/16/6. The work of E. Weyer was supported by the ARC under Discovery Grant DP130104028.

Author:

Carè Algo,Campi Marco C.,Csáji Balázs Cs.,Weyer Erik

Publisher

Elsevier BV

Subject

Control and Systems Engineering

Reference13 articles.

1. Non-asymptotic confidence regions for model parameters in the presence of unmodelled dynamics;Campi;Automatica,2009

2. Sign-Perturbed Sums (SPS) with asymmetric noise: Robustness analysis and robustification techniques;Carè;In Procs. of the 55th IEEE Conf. on Decision and Control,2016

3. A method for constructing exact finite-sample confidence regions for general linear systems;Csáji;In Procs. of the 51st IEEE Conference on Decision and Control,2012

4. Non-asymptotic confidence regions for the least-squares estimate;Csáji;In Procs. of the 16th IFAC Symposium on System Identification,2012

5. Strong consistency of the Sign-Perturbed Sums method;Csáji;In Procs. of the 53th IEEE Conference on Decision and Control,2014

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1. Facing undermodelling in Sign-Perturbed-Sums system identification;Systems & Control Letters;2021-07

2. Kernel-based SPS;IFAC-PapersOnLine;2018

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