IDEAS home Printed from https://ideas.repec.org/a/eee/apmaco/v488y2025ics0096300324005630.html
   My bibliography  Save this article

Parameter estimation method for separable fractional-order Hammerstein nonlinear systems based on the on-line measurements

Author

Listed:
  • Wang, Junwei
  • Xiong, Weili
  • Ding, Feng
  • Zhou, Yihong
  • Yang, Erfu

Abstract

This paper investigates the problem of parameter estimation for fractional-order Hammerstein nonlinear systems. To handle the identification difficulty of the parameters of the system and the order, the maximum likelihood and hierarchical identification principles are combined to derive a maximum likelihood gradient-based iterative algorithm. Moreover, to achieve the higher estimation accuracy, the multi-innovation identification theory is introduced, based on which the residual can be formulated as a linear combination of the innovation. Then, a multi-innovation maximum likelihood gradient-based iterative algorithm is proposed, which further improves the innovation utilization. Meanwhile, the computational cost of the proposed algorithm is assessed through the use of flops, which is less than those of its peers. Finally, the convergence analysis and simulation examples demonstrate the efficacy and robustness of the proposed algorithms.

Suggested Citation

  • Wang, Junwei & Xiong, Weili & Ding, Feng & Zhou, Yihong & Yang, Erfu, 2025. "Parameter estimation method for separable fractional-order Hammerstein nonlinear systems based on the on-line measurements," Applied Mathematics and Computation, Elsevier, vol. 488(C).
    • Handle: RePEc:eee:apmaco:v:488:y:2025:i:c:s0096300324005630
    DOI: 10.1016/j.amc.2024.129102
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0096300324005630
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.amc.2024.129102?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.

    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:488:y:2025:i:c:s0096300324005630. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.