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A New Partially-coupled Recursive Least Squares Algorithm for Multivariate Equation-error Systems

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  • Control Theory and Applications
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Abstract

This paper focuses on the parameter estimation problems for multivariate pseudo-linear systems. Based on the parameters coupling characteristic of the system model, a new partially-coupled least squares algorithm is proposed. For convenience of comparison, the traditional least squares algorithm for multivariate systems is given. The proposed algorithm has better performances than the traditional algorithm. The calculation amounts of the two algorithms are analyzed, it is shown that the proposed algorithm has better computational efficiency. Two numerical simulation examples are given, and the results indicate that the proposed algorithm has better parameters identification accuracy.

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Authors and Affiliations

  1. Jiangsu Key Laboratory of Media Design and Software Technology, School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, 214122, China

    Ping Ma

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Correspondence to Ping Ma.

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No potential conflict of interest was reported by the author.

Ping Ma received her Ph.D. degree from Jiangnan University, Wuxi, China in 2020. She is currently a Lecturer with the School of Artificial Intelligence and Computer Science at Jiangnan University. In 2018, she was sponsored by the China Scholarship Council as a joint Ph.D. candidate for one year with the University of Southampton, Southampton, UK. Her research interests include system identification and artificial intelligence.

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This work was supported by the National Natural Science Foundation of China (No. 61873121) and the Fundamental Research Funds for the Central Universities (No. JUSRP121071) and the ‘Taihu Light’ Basic Research Project on Scientific and Technological Breakthroughs of Wuxi City (No. K20221006).

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Ma, P. A New Partially-coupled Recursive Least Squares Algorithm for Multivariate Equation-error Systems. Int. J. Control Autom. Syst. 21, 1828–1839 (2023). https://doi.org/10.1007/s12555-022-0080-1

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  • DOI: https://doi.org/10.1007/s12555-022-0080-1

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