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Auxiliary Variable-Based Identification Algorithms for Uncertain-Input Models

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Abstract

This study presents two auxiliary variable-based identification algorithms for uncertain-input models. The auxiliary variable-based least squares algorithm can obtain unbiased parameter estimates by introducing suitable auxiliary variable vectors. Furthermore, an auxiliary variable-based recursive least squares algorithm is proposed to reduce the computational efforts. To validate the framework and algorithms developed, it has conducted a series of bench tests with computational experiments. The simulated numerical results/plots are consistent with the analytically derived results in terms of the feasibility and effectiveness of the proposed procedure.

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Authors and Affiliations

  1. School of Science, Jiangnan University, Wuxi, 214122, People’s Republic of China

    Jing Chen & Yan Pu

  2. Department of Engineering Design and Mathematics, University of the West of England, Bristol, BS16 1QY, UK

    Quanmin Zhu & Budi Chandra

Authors
  1. Jing Chen
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  2. Quanmin Zhu
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  3. Budi Chandra
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  4. Yan Pu
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Correspondence to Jing Chen.

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This work was supported by the National Natural Science Foundation of China (No. 61973137) and the Joint Funds of the National Natural Science Foundation of China (No. U1734210).

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Chen, J., Zhu, Q., Chandra, B. et al. Auxiliary Variable-Based Identification Algorithms for Uncertain-Input Models. Circuits Syst Signal Process 39, 3389–3404 (2020). https://doi.org/10.1007/s00034-019-01320-w

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  • DOI: https://doi.org/10.1007/s00034-019-01320-w

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