Circuits, Systems, and Signal Processing

, Volume 37, Issue 5, pp 1884–1906 | Cite as

Recursive and Iterative Least Squares Parameter Estimation Algorithms for Multiple-Input–Output-Error Systems with Autoregressive Noise

  • Jiling Ding


This paper considers the parameter estimation of a multiple-input–output-error system with autoregressive noise. In order to solve the problem of the information vector containing unknown inner variables, an auxiliary model-based recursive generalized least squares algorithm and a least squares-based iterative algorithm are proposed according to the auxiliary model identification idea and the iterative search principle. The simulation results indicate that the least squares-based iterative algorithm can generate more accurate parameter estimates than the auxiliary model-based recursive generalized least squares algorithm. Two examples are given to test the proposed algorithms.


Iterative algorithm Parameter estimation Least squares Multivariable system Auxiliary model 



This work was supported by the Natural Science Foundation of Shandong Province (China, ZR2016FL08) and the Science Foundation of Jining University (China, 2016QNKJ01).


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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Department of MathematicsJining UniversityQufuPeople’s Republic of China
  2. 2.School of Internet of Things EngineeringJiangnan UniversityWuxiPeople’s Republic of China

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