IDEAS home Printed from https://ideas.repec.org/a/eee/matcom/v182y2021icp765-790.html
   My bibliography  Save this article

Finite-time event-triggered approach for recurrent neural networks with leakage term and its application

Author

Listed:
  • Vadivel, R.
  • Hammachukiattikul, Porpattama
  • Rajchakit, G.
  • Syed Ali, M.
  • Unyong, Bundit

Abstract

This work investigates the finite-time event-triggered approach for recurrent neural networks with leakage term and its application. Here, decentralized event-triggered framework is recommended where event is checked at every sensor node related to local information for available triggering and the updated control is done whenever a centralized event is triggered. By handling the Lyapunov–Krasovskii functional (LKF) method together with novel inequality techniques like Wirtinger single and double integral inequality (WSI,WDI) technique, delay productive term (DPT), and a few adequate conditions are acquired to ensure the finite-time stability (FTS) analysis for the considered system, which is expressed with respect to linear matrix inequalities (LMIs). At last, numerical simulations are provided to indicate the efficiency of the expected results, two of these examples were supported by genuine use of the benchmark issue that correlates with sensible concerns under finite-time execution.

Suggested Citation

  • Vadivel, R. & Hammachukiattikul, Porpattama & Rajchakit, G. & Syed Ali, M. & Unyong, Bundit, 2021. "Finite-time event-triggered approach for recurrent neural networks with leakage term and its application," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 182(C), pages 765-790.
    • Handle: RePEc:eee:matcom:v:182:y:2021:i:c:p:765-790
    DOI: 10.1016/j.matcom.2020.12.001
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378475420304560
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.matcom.2020.12.001?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. Liu, Hailin & Chen, Guohua, 2007. "Delay-dependent stability for neural networks with time-varying delay," Chaos, Solitons & Fractals, Elsevier, vol. 33(1), pages 171-177.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jianying Xiao & Yongtao Li, 2022. "Novel Synchronization Conditions for the Unified System of Multi-Dimension-Valued Neural Networks," Mathematics, MDPI, vol. 10(17), pages 1-24, August.
    2. Guo, Runan & Xu, Shengyuan, 2023. "Observer-based sliding mode synchronization control of complex-valued neural networks with inertial term and mixed time-varying delays," Applied Mathematics and Computation, Elsevier, vol. 442(C).
    3. Karnan, A. & Nagamani, G., 2022. "Non-fragile state estimation for memristive cellular neural networks with proportional delay," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 193(C), pages 217-231.
    4. Shafiya, M. & Nagamani, G. & Dafik, D., 2022. "Global synchronization of uncertain fractional-order BAM neural networks with time delay via improved fractional-order integral inequality," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 191(C), pages 168-186.
    5. Zhang, Xiulan & Shi, Jiangteng & Liu, Heng & Chen, Fangqi, 2024. "Adaptive fuzzy event-triggered cooperative control for fractional-order delayed multi-agent systems with unknown control direction," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 220(C), pages 552-566.
    6. Zhang, Hai & Cheng, Yuhong & Zhang, Hongmei & Zhang, Weiwei & Cao, Jinde, 2022. "Hybrid control design for Mittag-Leffler projective synchronization on FOQVNNs with multiple mixed delays and impulsive effects," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 197(C), pages 341-357.

    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. Cui, Kaiyan & Song, Zhanjie & Zhang, Shuo, 2022. "Stability of neutral-type neural network with Lévy noise and mixed time-varying delays," Chaos, Solitons & Fractals, Elsevier, vol. 159(C).
    2. Zhang, Chuan-Ke & He, Yong & Jiang, Lin & Lin, Wen-Juan & Wu, Min, 2017. "Delay-dependent stability analysis of neural networks with time-varying delay: A generalized free-weighting-matrix approach," Applied Mathematics and Computation, Elsevier, vol. 294(C), pages 102-120.
    3. Tian, Junkang & Xu, Dongsheng, 2009. "New asymptotic stability criteria for neural networks with time-varying delays," Chaos, Solitons & Fractals, Elsevier, vol. 41(4), pages 1916-1922.
    4. Raja, R. & Zhu, Quanxin & Senthilraj, S. & Samidurai, R., 2015. "Improved stability analysis of uncertain neutral type neural networks with leakage delays and impulsive effects," Applied Mathematics and Computation, Elsevier, vol. 266(C), pages 1050-1069.
    5. Maharajan, C. & Raja, R. & Cao, Jinde & Rajchakit, G. & Alsaedi, Ahmed, 2018. "Novel results on passivity and exponential passivity for multiple discrete delayed neutral-type neural networks with leakage and distributed time-delays," Chaos, Solitons & Fractals, Elsevier, vol. 115(C), pages 268-282.
    6. Feng, Wei & Yang, Simon X. & Wu, Haixia, 2009. "On robust stability of uncertain stochastic neural networks with distributed and interval time-varying delays," Chaos, Solitons & Fractals, Elsevier, vol. 42(4), pages 2095-2104.
    7. Zong, Guangdeng & Liu, Jia, 2009. "New delay-dependent global robust stability conditions for interval neural networks with time-varying delays," Chaos, Solitons & Fractals, Elsevier, vol. 42(5), pages 2954-2964.
    8. Wang, Yao & Guo, Jun & Liu, Guobao & Lu, Junwei & Li, Fangyuan, 2021. "Finite-time sampled-data synchronization for uncertain neutral-type semi-Markovian jump neural networks with mixed time-varying delays," Applied Mathematics and Computation, Elsevier, vol. 403(C).
    9. Zhang, Hongmei & Cao, Jinde & Xiong, Lianglin, 2019. "Novel synchronization conditions for time-varying delayed Lur’e system with parametric uncertainty," Applied Mathematics and Computation, Elsevier, vol. 350(C), pages 224-236.
    10. Lee, S.H. & Park, M.J. & Kwon, O.M. & Choi, S.G., 2022. "Less conservative stability criteria for general neural networks through novel delay-dependent functional," Applied Mathematics and Computation, Elsevier, vol. 420(C).
    11. Hu, Jiming, 2009. "Synchronization conditions for chaotic nonlinear continuous neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 41(5), pages 2495-2501.

    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:182:y:2021:i:c:p:765-790. 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.