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Maximum Likelihood-based Multi-innovation Stochastic Gradient Method for Multivariable Systems

  • Huafeng XiaEmail author
  • Yan Ji
  • Yanjun Liu
  • Ling Xu
Article
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Abstract

This paper considers the parameter estimation problems for multivariable controlled autoregressive moving average systems. By means of the decomposition technique, a multivariable system is transformed into several identification submodels according to the number of outputs. A maximum likelihood extended stochastic gradient identification algorithm is derived for identifying each subsystem by using the maximum likelihood principle. In order to improve the convergence rate, a multivariable maximum likelihood-based muti-innovation stochastic gradient algorithm is proposed. The proposed algorithms can generate more accurate parameter estimates compared with the multivariable extended stochastic gradient algorithm. The illustrative simulation results show that the proposed methods work well.

Keywords

Maximum likelihood multi-innovation identification theory multivariable system parameter estimation stochastic gradient 

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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 2019

Authors and Affiliations

  1. 1.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.Taizhou Electric Power Conversion and Control Engineering Technology Research CenterTaizhou UniversityTaizhouP. R. China
  4. 4.Department of MathematicsKing Abdulaziz UniversityJeddahSaudi Arabia

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