Randomised subspace system identification: complexities and error bounds

Recently, a novel randomised subspace system identification method (RandSID) for estimating LTI state-space models was proposed in Kedia, V., & Chakraborty, D. (2023), which aimed to address the computational issues faced by traditional subspace identification methods when the recorded dataset i...

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Vydáno v:International journal of control Ročník 98; číslo 12; s. 2835 - 2846
Hlavní autoři: Kedia, Vatsal, Chakraborty, Debraj
Médium: Journal Article
Jazyk:angličtina
Vydáno: Abingdon Taylor & Francis 02.12.2025
Taylor & Francis Ltd
Témata:
Algorithms
Identification for Control
Identification methods
Randomised algorithms
Randomization
State-space modeling
Subspace Identification
Subspaces
System Identification
ISSN:0020-7179, 1366-5820
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Shrnutí:Recently, a novel randomised subspace system identification method (RandSID) for estimating LTI state-space models was proposed in Kedia, V., & Chakraborty, D. (2023), which aimed to address the computational issues faced by traditional subspace identification methods when the recorded dataset is large. In this article, we propose a modified version of RandSID and analyse the complexity of this proposed algorithm in terms of memory cost, data movement, flop count and computation time, and quantitatively establish the computational advantages over conventional algorithms. Further, we derive a bound on the ratio of mean squared errors for the proposed and conventional algorithms, thereby characterising the loss of accuracy specifically due to randomisation. The effectiveness of the proposed algorithm is established by various real and simulated case studies.
Bibliografie:ObjectType-Article-1
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ISSN:0020-7179
1366-5820
DOI:10.1080/00207179.2025.2485169