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Filtering-Based Parameter Identification Methods for Multivariable Stochastic Systems

Author

Listed:
  • Huafeng Xia

    (Taizhou Electric Power Conversion and Control Engineering Technology Research Center, Taizhou University, Taizhou 225300, China)

  • Feiyan Chen

    (Department of Mathematical Sciences, Xi’an Jiaotong Liverpool University, Suzhou 215123, China)

Abstract

This paper presents an adaptive filtering-based maximum likelihood multi-innovation extended stochastic gradient algorithm to identify multivariable equation-error systems with colored noises. The data filtering and model decomposition techniques are used to simplify the structure of the considered system, in which a predefined filter is utilized to filter the observed data, and the multivariable system is turned into several subsystems whose parameters appear in the vectors. By introducing the multi-innovation identification theory to the stochastic gradient method, this study produces improved performances. The simulation numerical results indicate that the proposed algorithm can generate more accurate parameter estimates than the filtering-based maximum likelihood recursive extended stochastic gradient algorithm.

Suggested Citation

  • Huafeng Xia & Feiyan Chen, 2020. "Filtering-Based Parameter Identification Methods for Multivariable Stochastic Systems," Mathematics, MDPI, vol. 8(12), pages 1-19, December.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:12:p:2254-:d:465556
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    References listed on IDEAS

    as
    1. Huafeng Xia & Yongqing Yang & Feng Ding & Ahmed Alsaedi & Tasawar Hayat, 2019. "Maximum likelihood-based recursive least-squares estimation for multivariable systems using the data filtering technique," International Journal of Systems Science, Taylor & Francis Journals, vol. 50(6), pages 1121-1135, April.
    2. Lujun Wang & Jiong Guo & Chen Xu & Tiezhou Wu & Huipin Lin, 2019. "Hybrid Model Predictive Control Strategy of Supercapacitor Energy Storage System Based on Double Active Bridge," Energies, MDPI, vol. 12(11), pages 1-20, June.
    3. Xue-Bo Jin & Hong-Xing Wang & Xiao-Yi Wang & Yu-Ting Bai & Ting-Li Su & Jian-Lei Kong, 2020. "Deep-Learning Prediction Model with Serial Two-Level Decomposition Based on Bayesian Optimization," Complexity, Hindawi, vol. 2020, pages 1-14, September.
    4. Ling Xu & Feng Ding & Quanmin Zhu, 2019. "Hierarchical Newton and least squares iterative estimation algorithm for dynamic systems by transfer functions based on the impulse responses," International Journal of Systems Science, Taylor & Francis Journals, vol. 50(1), pages 141-151, January.
    5. Yu Zhang & Zhe Yan & Cui Cui Zhou & Tie Zhou Wu & Yue Yang Wang, 0. "Capacity allocation of HESS in micro-grid based on ABC algorithm," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 15(4), pages 496-505.
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    Cited by:

    1. Hasan Raza & Ishtiaq Ahmad & Noor M. Khan & Waseem Abbasi & Muhammad Shahid Anwar & Sadique Ahmad & Mohammed A. El-Affendi, 2022. "Validation of Parallel Distributed Adaptive Signal Processing (PDASP) Framework through Processing-Inefficient Low-Cost Platforms," Mathematics, MDPI, vol. 10(23), pages 1-11, December.

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