IDEAS home Printed from https://ideas.repec.org/a/eee/apmaco/v413y2022ics0096300321007049.html
   My bibliography  Save this article

System identification of hammerstein models by using backward shift algorithm

Author

Listed:
  • Mi, Wen
  • Qian, Tao

Abstract

In this paper, a new identification method for discrete-time Hammerstein systems is proposed. The method is a joint use of discrete Fourier transform, backward shift method, and the least squares method. The frequency responses are obtained with sampled input and output data in the time domain through discrete Fourier transform. It is followed by the backward shift algorithm that was originally developed for estimating poles of linear time-invariant systems. After poles of linear subsystem are estimated, coefficients of linear and nonlinear subsystems are respectively determined by using the least squares (LS) method. The robustness of the backward shift algorithm guarantees the effectiveness of the proposed algorithm. Simulation results show that the poles of linear subsystem are well located. Thus, it is practical to identify discrete Hammerstein systems.

Suggested Citation

  • Mi, Wen & Qian, Tao, 2022. "System identification of hammerstein models by using backward shift algorithm," Applied Mathematics and Computation, Elsevier, vol. 413(C).
    • Handle: RePEc:eee:apmaco:v:413:y:2022:i:c:s0096300321007049
    DOI: 10.1016/j.amc.2021.126620
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0096300321007049
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.amc.2021.126620?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. Ling Xu & Feng Ding & Quanmin Zhu, 2021. "Decomposition strategy-based hierarchical least mean square algorithm for control systems from the impulse responses," International Journal of Systems Science, Taylor & Francis Journals, vol. 52(9), pages 1806-1821, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wang, Ziyun & Wang, Xianzhe & Wang, Yan, 2024. "Orthotope-search-expansion-based extended zonotopic Kalman filter design for a discrete-time linear parameter-varying system with a dual-noise term," Applied Mathematics and Computation, Elsevier, vol. 474(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ce Zhang & Xiangxiang Meng & Yan Ji, 2023. "Parameter Estimation of Fractional Wiener Systems with the Application of Photovoltaic Cell Models," Mathematics, MDPI, vol. 11(13), pages 1-22, June.
    2. Huafeng Xia & Feiyan Chen, 2020. "Filtering-Based Parameter Identification Methods for Multivariable Stochastic Systems," Mathematics, MDPI, vol. 8(12), pages 1-19, December.
    3. Xue-Bo Jin & Wen-Tao Gong & Jian-Lei Kong & Yu-Ting Bai & Ting-Li Su, 2022. "PFVAE: A Planar Flow-Based Variational Auto-Encoder Prediction Model for Time Series Data," Mathematics, MDPI, vol. 10(4), pages 1-17, February.
    4. Naveed Ahmed Malik & Ching-Lung Chang & Naveed Ishtiaq Chaudhary & Muhammad Asif Zahoor Raja & Khalid Mehmood Cheema & Chi-Min Shu & Sultan S. Alshamrani, 2022. "Knacks of Fractional Order Swarming Intelligence for Parameter Estimation of Harmonics in Electrical Systems," Mathematics, MDPI, vol. 10(9), pages 1-20, May.
    5. Hao Ma & Jian Pan & Lei Lv & Guanghui Xu & Feng Ding & Ahmed Alsaedi & Tasawar Hayat, 2019. "Recursive Algorithms for Multivariable Output-Error-Like ARMA Systems," Mathematics, MDPI, vol. 7(6), pages 1-18, June.
    6. Junxia Ma & Qiuling Fei & Fan Guo & Weili Xiong, 2019. "Variational Bayesian Iterative Estimation Algorithm for Linear Difference Equation Systems," Mathematics, MDPI, vol. 7(12), pages 1-16, November.
    7. Feng Ding & Jian Pan & Ahmed Alsaedi & Tasawar Hayat, 2019. "Gradient-Based Iterative Parameter Estimation Algorithms for Dynamical Systems from Observation Data," Mathematics, MDPI, vol. 7(5), pages 1-15, May.
    8. Zhou, Yihong & Zhang, Xiao & Ding, Feng, 2022. "Partially-coupled nonlinear parameter optimization algorithm for a class of multivariate hybrid models," Applied Mathematics and Computation, Elsevier, vol. 414(C).
    9. Jing, Shaoxue, 2023. "Time-delay Hammerstein system identification using modified cross-correlation method and variable stacking length multi-error algorithm," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 207(C), pages 288-300.
    10. Jianlei Kong & Hongxing Wang & Chengcai Yang & Xuebo Jin & Min Zuo & Xin Zhang, 2022. "A Spatial Feature-Enhanced Attention Neural Network with High-Order Pooling Representation for Application in Pest and Disease Recognition," Agriculture, MDPI, vol. 12(4), pages 1-30, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:apmaco:v:413:y:2022:i:c:s0096300321007049. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/applied-mathematics-and-computation .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.