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Least Squares based Iterative Parameter Estimation Algorithm for Stochastic Dynamical Systems with ARMA Noise Using the Model Equivalence

  • Feng Ding
  • Dandan Meng
  • Jiyang Dai
  • Qishen Li
  • Ahmed Alsaedi
  • Tasawar Hayat
Regular Paper Control Theory and Applications

Abstract

By means of the model equivalence theory, this paper proposes a model equivalence based least squares iterative algorithm for estimating the parameters of stochastic dynamical systems with ARMA noise. The proposed algorithm reduces the number of the unknown noise terms in the information vector and can give more accurate parameter estimates compared with the generalized extended least squares algorithm. The validity of the proposed method is evaluated through a numerical example.

Keywords

Dynamical system iterative method least squares model equivalence parameter estimation 

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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

  • Feng Ding
    • 1
    • 2
  • Dandan Meng
    • 2
  • Jiyang Dai
    • 1
  • Qishen Li
    • 1
  • Ahmed Alsaedi
    • 3
  • Tasawar Hayat
    • 3
    • 4
  1. 1.Nondestructive Test Key Laboratory of Ministry EducationNanchang Hangkong UniversityNanchangP. R. China
  2. 2.School of Internet of Things EngineeringJiangnan UniversityWuxiP. R. China
  3. 3.NAAM Research Group, Department of Mathematics, Faculty of ScienceKing Abdulaziz UniversityJeddahSaudi Arabia
  4. 4.Department of MathematicsQuaid-I-Azam UniversityIslamabadPakistan

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