Work

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

Year: 2024

Type: article

Abstract: 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.

Source: Automatica

DOI: 10.1016/j.automatica.2024.111991

Authors: Jing Chen, Yawen Mao, Dongqing Wang, Min Gan, Quanmin Zhu, Feng Liu

Institutions: Jiangnan University, Qingdao University, University of the West of England, Stevens Institute of Technology

Language: English

Topic: Control Systems and Identification

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,610

Funders: National Natural Science Foundation of China, Natural Science Foundation of Jiangsu Province, Taishan Scholar Project of Shandong Province