Reduced-order identification methods: Hierarchical algorithm or variable elimination algorithm

Jing Chen

doi:10.1016/j.automatica.2024.111991

Reduced-order identification methods: Hierarchical algorithm or variable elimination algorithm

2025-02-01

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DOI：10.1016/j.automatica.2024.111991

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Reduced-order identification algorithms are usually used in machine learning and big data technologies, where the large-scale systems widely exist. For large-scale system identification, traditional least squares algorithm involves high-order matrix inverse calculation, while traditional gradient descent algorithm has slow convergence rates. The reduced-order algorithm proposed in this paper has some advantages over the previous work: (1) via sequential partitioning of the parameter vector, the calculation of the inverse of a high-order matrix can be reduced to low-order matrix inverse calculations; (2) has a better conditioned information matrix than that of the gradient descent algorithm, thus has faster convergence rates; (3) its convergence rates can be increased by using the Aitken acceleration method, therefore the reduced-order based Aitken algorithm is at least quadratic convergent and has no limitation on the step-size. The properties of the reduced-order algorithm are also given. Simulation results demonstrate the effectiveness of the proposed algorithm. (c) 2024 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

Keywords:

Reduced-order algorithm

Least square algorithm

Gradient descent algorithm

Condition number

Hierarchical identification algorithm

Variable elimination algorithm

Reduced-order identification methods: Hierarchical algorithm or variable elimination algorithm

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