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Circuits, Systems, and Signal Processing

, Volume 38, Issue 2, pp 590–610 | Cite as

Auxiliary Model-Based Recursive Generalized Least Squares Algorithm for Multivariate Output-Error Autoregressive Systems Using the Data Filtering

  • Qinyao Liu
  • Feng DingEmail author
Article
  • 80 Downloads

Abstract

This paper focuses on the parameter estimation problem of multivariate output-error autoregressive systems. Based on the data filtering technique and the auxiliary model identification idea, we derive a filtering-based auxiliary model recursive generalized least squares algorithm. The key is to filter the input–output data and to derive two identification models, one of which includes the system parameters and the other contains the noise parameters. Compared with the auxiliary model-based recursive generalized least squares algorithm, the proposed algorithm requires less computational burden and can generate more accurate parameter estimates. Finally, an illustrative example is provided to verify the effectiveness of the proposed algorithm.

Keywords

Filtering technique Parameter estimation Recursive least squares Multivariate system Auxiliary model 

Notes

Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 61273194) and the 111 Project (B12018).

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education)Jiangnan UniversityWuxiPeople’s Republic of China
  2. 2.College of Automation and Electronic EngineeringQingdao University of Science and TechnologyQingdaoPeople’s Republic of China

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