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Electrical Engineering and Systems Science > Systems and Control

arXiv:2309.03873 (eess)
[Submitted on 7 Sep 2023 (v1), last revised 16 Jun 2024 (this version, v2)]

Title:A Tutorial on the Non-Asymptotic Theory of System Identification

Authors:Ingvar Ziemann, Anastasios Tsiamis, Bruce Lee, Yassir Jedra, Nikolai Matni, George J. Pappas
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Abstract:This tutorial serves as an introduction to recently developed non-asymptotic methods in the theory of -- mainly linear -- system identification. We emphasize tools we deem particularly useful for a range of problems in this domain, such as the covering technique, the Hanson-Wright Inequality and the method of self-normalized martingales. We then employ these tools to give streamlined proofs of the performance of various least-squares based estimators for identifying the parameters in autoregressive models. We conclude by sketching out how the ideas presented herein can be extended to certain nonlinear identification problems.
Subjects: Systems and Control (eess.SY); Machine Learning (cs.LG); Machine Learning (stat.ML)
Cite as: arXiv:2309.03873 [eess.SY]
  (or arXiv:2309.03873v2 [eess.SY] for this version)
  https://doi.org/10.48550/arXiv.2309.03873

Submission history

From: Ingvar Ziemann [view email]
[v1] Thu, 7 Sep 2023 17:33:30 UTC (92 KB)
[v2] Sun, 16 Jun 2024 21:50:01 UTC (90 KB)
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