IDEAS home Printed from https://ideas.repec.org/a/eee/matcom/v207y2023icp482-498.html
   My bibliography  Save this article

A noise tolerant parameter-variable zeroing neural network and its applications

Author

Listed:
  • Jin, Jie
  • Chen, Weijie
  • Qiu, Lixin
  • Zhu, Jingcan
  • Liu, Haiyan

Abstract

Time-varying problems frequently arise in the territories of science and engineering, and most of the time-varying problems can be described by dynamic matrix equations. As a powerful tool for solving dynamic matrix equations, the zeroing neural network (ZNN) develops fast in recent years. Convergence and robustness are two main performance indicators of the ZNN model. However, the development of the ZNN is focused on the improvement of its convergence in the past, and its robustness to noises is rarely considered. In order to achieve fast convergence and robustness of the ZNN model, a novel activation function (NAF) is presented in this paper. Based on the NAF, a noise-tolerant parameter-variable ZNN (NTPVZNN) model for solving dynamic Sylvester matrix equations (DSME) is realized, and its fixed-time convergence and robustness to noises are verified by rigorous mathematical analysis and numerical simulation results. Besides, two examples of electrical circuit currents computing and robotic manipulator trajectory tracking using the proposed NTPVZNN model in noisy environment further demonstrates its practical application ability.

Suggested Citation

  • Jin, Jie & Chen, Weijie & Qiu, Lixin & Zhu, Jingcan & Liu, Haiyan, 2023. "A noise tolerant parameter-variable zeroing neural network and its applications," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 207(C), pages 482-498.
    • Handle: RePEc:eee:matcom:v:207:y:2023:i:c:p:482-498
    DOI: 10.1016/j.matcom.2023.01.012
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378475423000125
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.matcom.2023.01.012?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. Sobehart, Lionel & Harada, Hiroyuki, 2018. "High performance rigid body simulation of modularized robots using constraint-based models," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 144(C), pages 91-107.
    2. Zhao, Lv & Jin, Jie & Gong, Jianqiang, 2021. "Robust zeroing neural network for fixed-time kinematic control of wheeled mobile robot in noise-polluted environment," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 185(C), pages 289-307.
    3. Xiao, Lin & Yi, Qian & Zuo, Qiuyue & He, Yongjun, 2020. "Improved finite-time zeroing neural networks for time-varying complex Sylvester equation solving," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 178(C), pages 246-258.
    4. My, Chu Anh & Makhanov, Stanislav S. & Van, Nguyen A. & Duc, Vu M., 2020. "Modeling and computation of real-time applied torques and non-holonomic constraint forces/moment, and optimal design of wheels for an autonomous security robot tracking a moving target," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 170(C), pages 300-315.
    5. Stanimirović, Predrag & Gerontitis, Dimitris & Tzekis, Panagiotis & Behera, Ratikanta & Sahoo, Jajati Keshari, 2021. "Simulation of Varying Parameter Recurrent Neural Network with application to matrix inversion," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 185(C), pages 614-628.
    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. Zhu, Jingcan & Jin, Jie & Chen, Weijie & Gong, Jianqiang, 2022. "A combined power activation function based convergent factor-variable ZNN model for solving dynamic matrix inversion," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 197(C), pages 291-307.
    2. Xiao, Lin & Li, Xiaopeng & Jia, Lei & Liu, Sai, 2022. "Improved finite-time solutions to time-varying Sylvester tensor equation via zeroing neural networks," Applied Mathematics and Computation, Elsevier, vol. 416(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:482-498. 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.