Journal
IET CONTROL THEORY AND APPLICATIONS
Volume 7, Issue 1, Pages 68-79Publisher
INST ENGINEERING TECHNOLOGY-IET
DOI: 10.1049/iet-cta.2012.0171
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Funding
- National Natural Science Foundation of China [61273194]
- Natural Science Foundation of Jiangsu Province China [BK2012549]
- 111 Project [B12018]
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This article studies identification problems of multiple linear regression models, which may be described a class of multi-input multi-output systems (i.e. multivariable systems). Based on the coupling identification concept, a novel coupled-least-squares (C-LS) parameter identification algorithm is introduced for the purpose of avoiding the matrix inversion in the multivariable recursive least-squares (RLS) algorithm for estimating the parameters of the multiple linear regression models. The analysis indicates that the C-LS algorithm does not involve the matrix inversion and requires less computationally efforts than the multivariable RLS algorithm, and that the parameter estimates given by the C-LS algorithm converge to their true values. Simulation results confirm the presented convergence theorems.
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