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Coupled Least Squares Identification Algorithms for Multivariate Output-Error Systems

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Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi 214122, China
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Academic Editor: Florin Manea
Algorithms 2017, 10(1), 12; https://doi.org/10.3390/a10010012
Received: 17 November 2016 / Revised: 5 January 2017 / Accepted: 6 January 2017 / Published: 12 January 2017
This paper focuses on the recursive identification problems for a multivariate output-error system. By decomposing the system into several subsystems and by forming a coupled relationship between the parameter estimation vectors of the subsystems, two coupled auxiliary model based recursive least squares (RLS) algorithms are presented. Moreover, in contrast to the auxiliary model based recursive least squares algorithm, the proposed algorithms provide a reference to improve the identification accuracy of the multivariate output-error system. The simulation results confirm the effectiveness of the proposed algorithms. View Full-Text
Keywords: coupling identification concept; parameter estimation; auxiliary model; least squares; multivariate system coupling identification concept; parameter estimation; auxiliary model; least squares; multivariate system
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Huang, W.; Ding, F. Coupled Least Squares Identification Algorithms for Multivariate Output-Error Systems. Algorithms 2017, 10, 12.

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