IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i20p4299-d1260491.html
   My bibliography  Save this article

The Maximum Correntropy Criterion-Based Identification for Fractional-Order Systems under Stable Distribution Noises

Author

Listed:
  • Yao Lu

    (School of Mathematics and Statistics, Northeastern University at Qinhuangdao, Qinhuangdao 066004, China)

Abstract

This paper studies the identification for fractional-order systems (FOSs) under stable distribution noises. First, the generalized operational matrix of block pulse functions is used to convert the identified system into an algebraic one. Then, the conventional least mean square (LMS) criterion is replaced by the maximum correntropy criterion (MCC) to restrain the effect of noises, and a MCC-based algorithm is designed to perform the identification. To verify the superiority of the proposed method, the identification accuracy is examined when the noise follows different types of stable distributions. In addition, the impact of parameters of stable distribution on identification accuracy is discussed. It is shown that when the impulse of noise increases, the identification error becomes larger, but the proposed algorithm is always superior to its LMS counterpart. Moreover, the location parameter of stable distribution noise has a significant impact on the identification accuracy.

Suggested Citation

  • Yao Lu, 2023. "The Maximum Correntropy Criterion-Based Identification for Fractional-Order Systems under Stable Distribution Noises," Mathematics, MDPI, vol. 11(20), pages 1-18, October.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:20:p:4299-:d:1260491
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/20/4299/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/11/20/4299/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhang, Xuefeng & Chen, Shunan & Zhang, Jin-Xi, 2022. "Adaptive sliding mode consensus control based on neural network for singular fractional order multi-agent systems," Applied Mathematics and Computation, Elsevier, vol. 434(C).
    2. Zhang, Tao & Lu, Zhong-rong & Liu, Ji-ke & Chen, Yan-mao & Liu, Guang, 2023. "Parameter estimation of linear fractional-order system from laplace domain data," Applied Mathematics and Computation, Elsevier, vol. 438(C).
    3. Xie, Bing & Ge, Fudong, 2023. "Parameters and order identification of fractional-order epidemiological systems by Lévy-PSO and its application for the spread of COVID-19," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
    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. Mehmood, Ammara & Raja, Muhammad Asif Zahoor & Ninness, Brett, 2024. "Design of fractional-order hammerstein control auto-regressive model for heat exchanger system identification: Treatise on fuzzy-evolutionary computing," Chaos, Solitons & Fractals, Elsevier, vol. 181(C).
    2. Chen, Jiqing & Zhang, Haiyan & Zhu, Tongtong & Pan, Shangtao, 2024. "Trajectory tracking control of a manipulator based on an immune algorithm-optimized neural network in the presence of unknown backlash-like hysteresis," Applied Mathematics and Computation, Elsevier, vol. 470(C).
    3. Arockia Samy, Stephen & Anbalagan, Pratap, 2023. "Disturbance observer-based integral sliding-mode control design for leader-following consensus of multi-agent systems and its application to car-following model," Chaos, Solitons & Fractals, Elsevier, vol. 174(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:gam:jmathe:v:11:y:2023:i:20:p:4299-:d:1260491. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.