A Tutorial on the Non-Asymptotic Theory of System Identification
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Date
2023
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Conference Paper
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yes
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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.
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published
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2023 62nd IEEE Conference on Decision and Control (CDC)
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Pages / Article No.
8921 - 8939
Publisher
IEEE
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62nd IEEE Conference on Decision and Control (CDC 2023)
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00002 - ETH Zürich