IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v12y2024i9p1409-d1388636.html
   My bibliography  Save this article

Event-Triggered Synchronization of Coupled Neural Networks with Reaction–Diffusion Terms

Author

Listed:
  • Abulajiang Aili

    (School of Mathematics and Statistics, Kashi University, Kashi 844006, China)

  • Shenglong Chen

    (College of Mathematics and System Science, Xinjiang University, Urumqi 830017, China)

  • Sibao Zhang

    (School of Mathematics and Statistics, Kashi University, Kashi 844006, China)

Abstract

This paper focuses on the event-triggered synchronization of coupled neural networks with reaction–diffusion terms. At first, an effective event-triggered controller was designed based on time sampling. It is worth noting that the data of the controller for this type can be updated only when corresponding triggering conditions are satisfied, which can significantly reduce the communication burden of the control systems compared to other control strategies. Furthermore, some sufficient criteria were obtained to ensure the event-triggered synchronization of the considered systems through the use of an inequality techniques as well as the designed controller. Finally, the validity of the theoretical results was confirmed using numerical examples.

Suggested Citation

  • Abulajiang Aili & Shenglong Chen & Sibao Zhang, 2024. "Event-Triggered Synchronization of Coupled Neural Networks with Reaction–Diffusion Terms," Mathematics, MDPI, vol. 12(9), pages 1-16, May.
  • Handle: RePEc:gam:jmathe:v:12:y:2024:i:9:p:1409-:d:1388636
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/12/9/1409/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/12/9/1409/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Luo, Lingao & Li, Lulu & Huang, Wei, 2024. "Asymptotic stability of fractional-order Hopfield neural networks with event-triggered delayed impulses and switching effects," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 219(C), pages 491-504.
    2. Mao, Kun & Liu, Xiaoyang & Cao, Jinde & Hu, Yuanfa, 2022. "Finite-time bipartite synchronization of coupled neural networks with uncertain parameters," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 585(C).
    3. Xupeng Luo & Haijun Jiang & Jiarong Li & Shanshan Chen & Yang Xia, 2024. "Modeling and controlling delayed rumor propagation with general incidence in heterogeneous networks," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 35(02), pages 1-29, February.
    4. Stamov, Gani & Stamova, Ivanka & Martynyuk, Anatoliy & Stamov, Trayan, 2021. "Almost periodic dynamics in a new class of impulsive reaction–diffusion neural networks with fractional-like derivatives," Chaos, Solitons & Fractals, Elsevier, vol. 143(C).
    5. Chen, Shenglong & Yang, Jikai & Li, Zhiming & Li, Hong-Li & Hu, Cheng, 2023. "New results for dynamical analysis of fractional-order gene regulatory networks with time delay and uncertain parameters," Chaos, Solitons & Fractals, Elsevier, vol. 175(P1).
    6. Luo, Mengzhuo & Cheng, Jun & Liu, Xinzhi & Zhong, Shouming, 2019. "An extended synchronization analysis for memristor-based coupled neural networks via aperiodically intermittent control," Applied Mathematics and Computation, Elsevier, vol. 344, pages 163-182.
    7. Dong, Tao & Wang, Aijuan & Zhu, Huiyun & Liao, Xiaofeng, 2018. "Event-triggered synchronization for reaction–diffusion complex networks via random sampling," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 495(C), pages 454-462.
    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. Liu, Xiaonan & Kao, Yonggui, 2021. "Aperiodically intermittent pinning outer synchronization control for delayed complex dynamical networks with reaction-diffusion terms," Applied Mathematics and Computation, Elsevier, vol. 410(C).
    2. Wang, Aijuan & Liao, Xiaofeng & Dong, Tao, 2018. "Finite-time event-triggered synchronization for reaction–diffusion complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 111-120.
    3. Cao, Yang & Udhayakumar, K. & Veerakumari, K. Pradeepa & Rakkiyappan, R., 2022. "Memory sampled data control for switched-type neural networks and its application in image secure communications," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 201(C), pages 564-587.
    4. Hu, Jingting & Sui, Guixia & Li, Xiaodi, 2020. "Fixed-time synchronization of complex networks with time-varying delays," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    5. Luo, Yiping & Yao, Yuejie & Cheng, Zifeng & Xiao, Xing & Liu, Hanyu, 2021. "Event-triggered control for coupled reaction–diffusion complex network systems with finite-time synchronization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 562(C).
    6. Fan, Hongguang & Shi, Kaibo & Zhao, Yi, 2022. "Global μ-synchronization for nonlinear complex networks with unbounded multiple time delays and uncertainties via impulsive control," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 599(C).
    7. Chen, Jing & Xiao, Min & Wu, Xiaoqun & Wang, Zhengxin & Cao, Jinde, 2022. "Spatiotemporal dynamics on a class of (n+1)-dimensional reaction–diffusion neural networks with discrete delays and a conical structure," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).
    8. Chen, Wei & Yu, Yongguang & Hai, Xudong & Ren, Guojian, 2022. "Adaptive quasi-synchronization control of heterogeneous fractional-order coupled neural networks with reaction-diffusion," Applied Mathematics and Computation, Elsevier, vol. 427(C).
    9. Wang, Yangling & Cao, Jinde & Wang, Haijun & Alsaadi, Fuad E., 2019. "Event-triggered consensus of multi-agent systems with nonlinear dynamics and communication delay," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 522(C), pages 147-157.
    10. Sun, Bo & Cao, Yuting & Guo, Zhenyuan & Yan, Zheng & Wen, Shiping, 2020. "Synchronization of discrete-time recurrent neural networks with time-varying delays via quantized sliding mode control," Applied Mathematics and Computation, Elsevier, vol. 375(C).
    11. Hongguang Fan & Jiahui Tang & Kaibo Shi & Yi Zhao & Hui Wen, 2023. "Delayed Impulsive Control for μ -Synchronization of Nonlinear Multi-Weighted Complex Networks with Uncertain Parameter Perturbation and Unbounded Delays," Mathematics, MDPI, vol. 11(1), pages 1-17, January.

    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:12:y:2024:i:9:p:1409-:d:1388636. 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.