Topological Linear System Identification via Moderate Deviations Theory

Jongeneel, Wouter; Sutter, Tobias; Kuhn, Daniel

Topological Linear System Identification via Moderate Deviations Theory

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Datum

2022

Autor:innen

Jongeneel, Wouter

Sutter, Tobias

Kuhn, Daniel

DOI (zitierfähiger Link)

https://doi.org/10.1109/LCSYS.2021.3072814

Sammlungen

Informatik und Informationswissenschaft

Publikationstyp

Zeitschriftenartikel

Publikationsstatus

Published

Erschienen in

IEEE Control Systems Letters. IEEE. 2022, 6, pp. 307-312. eISSN 2475-1456. Available under: doi: 10.1109/LCSYS.2021.3072814

Zusammenfassung

Two dynamical systems are topologically equivalent when their phase-portraits can be morphed into each other by a homeomorphic coordinate transformation on the state space. The induced equivalence classes capture qualitative properties such as stability or the oscillatory nature of the state trajectories, for example. In this letter we develop a method to learn the topological class of an unknown stable system from a single trajectory of finitely many state observations. Using a moderate deviations principle for the least squares estimator of the unknown system matrix θ, we prove that the probability of misclassification decays exponentially with the number of observations at a rate that is proportional to the square of the smallest singular value of θ.

Fachgebiet (DDC)

004 Informatik

Zitieren

ISO 690

JONGENEEL, Wouter, Tobias SUTTER, Daniel KUHN, 2022. Topological Linear System Identification via Moderate Deviations Theory. In: IEEE Control Systems Letters. IEEE. 2022, 6, pp. 307-312. eISSN 2475-1456. Available under: doi: 10.1109/LCSYS.2021.3072814

BibTex

@article{Jongeneel2022Topol-55610,
  year={2022},
  doi={10.1109/LCSYS.2021.3072814},
  title={Topological Linear System Identification via Moderate Deviations Theory},
  volume={6},
  journal={IEEE Control Systems Letters},
  pages={307--312},
  author={Jongeneel, Wouter and Sutter, Tobias and Kuhn, Daniel}
}

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