IDEAS home Printed from https://ideas.repec.org/a/eee/matcom/v207y2023icp288-300.html
   My bibliography  Save this article

Time-delay Hammerstein system identification using modified cross-correlation method and variable stacking length multi-error algorithm

Author

Listed:
  • Jing, Shaoxue

Abstract

In this paper, the identification of a time delay Hammerstein system is considered. Based on a normalized Gaussian white input, a cross-correlation function increment method is investigated to estimate the time delay without the parameter estimates. The integer delay is obtained directly, not by rounding a decimal delay estimate. To identify the parameters, a novel multi-error information gradient algorithm with variable stacking length is proposed. In the parameter identification algorithm, the slow stochastic information gradient algorithm is accelerated by a stacked error named multi-error and the variable stacking length is determined by a loss function descent criterion. Several simulation experiments validate the proposed algorithm.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:matcom:v:207:y:2023:i:c:p:288-300
    DOI: 10.1016/j.matcom.2022.12.031
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378475422005195
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.matcom.2022.12.031?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. Abubakar, Auwal Bala & Kumam, Poom & Ibrahim, Abdulkarim Hassan & Chaipunya, Parin & Rano, Sadiya Ali, 2022. "New hybrid three-term spectral-conjugate gradient method for finding solutions of nonlinear monotone operator equations with applications," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 201(C), pages 670-683.
    2. 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).
    3. Wei, Yun & Tian, Qing & Guo, Jianhua & Huang, Wei & Cao, Jinde, 2019. "Multi-vehicle detection algorithm through combining Harr and HOG features," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 155(C), pages 130-145.
    4. Abubakar, Auwal Bala & Kumam, Poom & Malik, Maulana & Ibrahim, Abdulkarim Hassan, 2022. "A hybrid conjugate gradient based approach for solving unconstrained optimization and motion control problems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 201(C), pages 640-657.
    5. 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)

    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. 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.
    2. 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.
    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. Junran Lin & Cuimei Yang & Yi Lu & Yuxing Cai & Hanjie Zhan & Zhen Zhang, 2022. "An Improved Soft-YOLOX for Garbage Quantity Identification," Mathematics, MDPI, vol. 10(15), pages 1-12, July.
    5. Mrad, Hatem & Fakhari, Seyyed Mojtaba, 2024. "Optimization of unconstrained problems using a developed algorithm of spectral conjugate gradient method calculation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 215(C), pages 282-290.
    6. Aleksey Osipov & Ekaterina Pleshakova & Sergey Gataullin & Sergey Korchagin & Mikhail Ivanov & Anton Finogeev & Vibhash Yadav, 2022. "Deep Learning Method for Recognition and Classification of Images from Video Recorders in Difficult Weather Conditions," Sustainability, MDPI, vol. 14(4), pages 1-16, February.
    7. Chaudhary, Naveed Ishtiaq & Khan, Zeshan Aslam & Kiani, Adiqa Kausar & Raja, Muhammad Asif Zahoor & Chaudhary, Iqra Ishtiaq & Pinto, Carla M.A., 2022. "Design of auxiliary model based normalized fractional gradient algorithm for nonlinear output-error systems," Chaos, Solitons & Fractals, Elsevier, vol. 163(C).
    8. 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.
    9. Tang, Jia, 2023. "Fractional gradient descent algorithm for switching models using self-organizing maps: One set data or all the collected data," Chaos, Solitons & Fractals, Elsevier, vol. 172(C).
    10. Mi, Wen & Qian, Tao, 2022. "System identification of hammerstein models by using backward shift algorithm," Applied Mathematics and Computation, Elsevier, vol. 413(C).

    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:matcom:v:207:y:2023:i:c:p:288-300. 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: http://www.journals.elsevier.com/mathematics-and-computers-in-simulation/ .

    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.