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

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Abstract

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.

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Acknowledgements

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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Authors and Affiliations

  1. Department of Mathematics, Jining University, Qufu, 273155, People’s Republic of China

    Jiling Ding

  2. School of Internet of Things Engineering, Jiangnan University, Wuxi, 214122, People’s Republic of China

    Jiling Ding

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Correspondence to Jiling Ding.

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Ding, J. Recursive and Iterative Least Squares Parameter Estimation Algorithms for Multiple-Input–Output-Error Systems with Autoregressive Noise. Circuits Syst Signal Process 37, 1884–1906 (2018). https://doi.org/10.1007/s00034-017-0636-0

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