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Recursive Identification Methods for Multivariate Output-error Moving Average Systems Using the Auxiliary Model

  • Qinyao Liu
  • Feng Ding
  • Ahmed Alsaedi
  • Tasawar Hayat
Regular Papers Control Theory and Applications
  • 63 Downloads

Abstract

This paper studies the parameter identification problems of multivariate output-error moving average systems. An auxiliary model based extended stochastic gradient algorithm and based recursive extended least squares algorithm are proposed for estimating the parameters of the multivariate output-error moving average systems. By using the multi-innovation identification theory, an auxiliary model based multi-innovation extended stochastic gradient algorithm is derived for improving the parameter estimation accuracy. Finally, the simulation results indicate that the proposed algorithms can work well.

Keywords

Auxiliary model multivariate system parameter estimation recursive identification 

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Copyright information

© Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Qinyao Liu
    • 1
  • Feng Ding
    • 1
  • Ahmed Alsaedi
    • 2
  • Tasawar Hayat
    • 2
    • 3
  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.NAAM Research Group, Department of Mathematics, Faculty of ScienceKing Abdulaziz UniversityJeddahSaudi Arabia
  3. 3.Department of MathematicsQuaid-I-Azam UniversityIslamabadPakistan

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