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Two-stage Recursive Least Squares Parameter Estimation Algorithm for Multivariate Output-error Autoregressive Moving Average Systems

  • Yunze Guo
  • Lijuan Wan
  • Ling Xu
  • Feng DingEmail author
  • Ahmed Alsaedi
  • Tasawar Hayat
Regular Papers Control Theory and Applications

Abstract

This paper focuses on the parameter estimation problem of multivariate output-error autoregressive moving average (M-OEARMA) systems. By applying the auxiliary model identification idea and the decomposition technique, we derive a two-stage recursive least squares algorithm for estimating the M-OEARMA system. Compared with the auxiliary model based recursive least squares algorithm, the proposed algorithm possesses higher identification accuracy. The simulation results confirm the effectiveness of the proposed algorithm.

Keywords

Auxiliary model decomposition technique least squares parameter estimation recursive identification 

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Copyright information

© ICROS, KIEE and Springer 2019

Authors and Affiliations

  1. 1.Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), School of Internet of Things EngineeringJiangnan UniversityWuxiP. R. China
  2. 2.College of Automation and Electronic EngineeringQingdao University of Science and TechnologyQingdaoP. R. China
  3. 3.school of Internet of Things TechnologyWuxi Vocational Institute of commerceWuxiP. R. China
  4. 4.Department of MathematicsKing Abdulaziz UniversityJeddahSaudi Arabia

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