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Recursive least squares identification methods for multivariate pseudo-linear systems using the data filtering

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Abstract

This paper concerns the parameter identification methods of multivariate pseudo-linear autoregressive systems. A multivariate recursive generalized least squares algorithm is presented as a comparison. By using the data filtering technique, a multivariate pseudo-linear autoregressive system is transformed into a filtered system model and a filtered noise model, and a filtering based multivariate recursive generalized least squares algorithm is developed for estimating the parameters of these two models. The proposed algorithm achieves a higher computational efficiency than the multivariate recursive generalized least squares algorithm, and the simulation results prove that the proposed method is effective.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 61273194) and the 111 Project (B12018).

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Authors and Affiliations

  1. Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), School of Internet of Things Engineering, Jiangnan University, Wuxi, 214122, China

    Ping Ma & Feng Ding

  2. Nonlinear Analysis and Applied Mathematics (NAAM) Research Group, Department of Mathematics, King Abdulaziz University, Jeddah, 21589, Saudi Arabia

    Feng Ding, Ahmed Alsaedi & Tasawar Hayat

  3. Department of Mathematics, Quaid-I-Azam University, Islamabad, 44000, Pakistan

    Tasawar Hayat

Authors
  1. Ping Ma
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  2. Feng Ding
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  3. Ahmed Alsaedi
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  4. Tasawar Hayat
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Corresponding author

Correspondence to Feng Ding.

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Ma, P., Ding, F., Alsaedi, A. et al. Recursive least squares identification methods for multivariate pseudo-linear systems using the data filtering. Multidim Syst Sign Process 29, 1135–1152 (2018). https://doi.org/10.1007/s11045-017-0491-y

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  • DOI: https://doi.org/10.1007/s11045-017-0491-y

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