IDEAS home Printed from https://ideas.repec.org/a/eee/apmaco/v438y2023ics0096300322006750.html
   My bibliography  Save this article

Exponential stability of neural networks with a time-varying delay via a cubic function negative-determination lemma

Author

Listed:
  • Chang, Xu-Kang
  • He, Yong
  • Gao, Zhen-Man

Abstract

The problem of global exponential stability of neural networks with a time-varying delay is studied in this article. Firstly, to fully utilize the cross-term relationships among state variables, an improved augmented delay-product-type Lyapunov-Krasovskii functional, including an extra double integral state, is established for the stability analysis. Accordingly, this augmented LKF derivative is a higher-order function of the time-varying delay. Then, three state vectors are considered to reduce the order of the function to cubic. So, to obtain the feasible negative-definiteness condition of this LKF derivative of non-convexity, a negative-determination lemma for cubic functions is employed to handle this problem. As a result, a novel stability criterion is obtained. Two well-known numerical examples illustrate the effectiveness of the criterion.

Suggested Citation

  • Chang, Xu-Kang & He, Yong & Gao, Zhen-Man, 2023. "Exponential stability of neural networks with a time-varying delay via a cubic function negative-determination lemma," Applied Mathematics and Computation, Elsevier, vol. 438(C).
  • Handle: RePEc:eee:apmaco:v:438:y:2023:i:c:s0096300322006750
    DOI: 10.1016/j.amc.2022.127602
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0096300322006750
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.amc.2022.127602?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. Zeng, Hong-Bing & Zhai, Zheng-Liang & Wang, Wei, 2021. "Hierarchical stability conditions of systems with time-varying delay," Applied Mathematics and Computation, Elsevier, vol. 404(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. de Oliveira, Fúlvia S.S. & Souza, Fernando O., 2020. "Further refinements in stability conditions for time-varying delay systems," Applied Mathematics and Computation, Elsevier, vol. 369(C).
    4. Ji, Meng-Di & He, Yong & Wu, Min & Zhang, Chuan-Ke, 2015. "Further results on exponential stability of neural networks with time-varying delay," Applied Mathematics and Computation, Elsevier, vol. 256(C), pages 175-182.
    5. Wang, Chen-Rui & He, Yong & Lin, Wen-Juan, 2021. "Stability analysis of generalized neural networks with fast-varying delay via a relaxed negative-determination quadratic function method," Applied Mathematics and Computation, Elsevier, vol. 391(C).
    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. Arunagirinathan, S. & Lee, T.H., 2024. "Generalized delay-dependent reciprocally convex inequality on stability for neural networks with time-varying delay," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 217(C), pages 109-120.

    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. Lee, Tae H. & Park, Myeong Jin & Park, Ju H., 2021. "An improved stability criterion of neural networks with time-varying delays in the form of quadratic function using novel geometry-based conditions," Applied Mathematics and Computation, Elsevier, vol. 404(C).
    2. Wang, Yibo & Hua, Changchun & Park, PooGyeon & Qian, Cheng, 2023. "Stability criteria for time-varying delay systems via an improved reciprocally convex inequality lemma," Applied Mathematics and Computation, Elsevier, vol. 448(C).
    3. Wang, Chen-Rui & He, Yong & Lin, Wen-Juan, 2021. "Stability analysis of generalized neural networks with fast-varying delay via a relaxed negative-determination quadratic function method," Applied Mathematics and Computation, Elsevier, vol. 391(C).
    4. 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.
    5. Shuoting Wang & Kaibo Shi & Jin Yang, 2022. "Improved Stability Criteria for Delayed Neural Networks via a Relaxed Delay-Product-Type Lapunov–Krasovskii Functional," Mathematics, MDPI, vol. 10(15), pages 1-14, August.
    6. Li, Lingchun & Shen, Mouquan & Zhang, Guangming & Yan, Shen, 2017. "H∞ control of Markov jump systems with time-varying delay and incomplete transition probabilities," Applied Mathematics and Computation, Elsevier, vol. 301(C), pages 95-106.
    7. Arunagirinathan, S. & Lee, T.H., 2024. "Generalized delay-dependent reciprocally convex inequality on stability for neural networks with time-varying delay," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 217(C), pages 109-120.
    8. Wang, Bo & Yan, Juan & Cheng, Jun & Zhong, Shouming, 2017. "New criteria of stability analysis for generalized neural networks subject to time-varying delayed signals," Applied Mathematics and Computation, Elsevier, vol. 314(C), pages 322-333.
    9. Liming Ding & Liqin Chen & Dajiang He & Weiwei Xiang, 2022. "New Delay-Partitioning LK-Functional for Stability Analysis with Neutral Type Systems," Mathematics, MDPI, vol. 10(21), pages 1-13, November.
    10. Chen, Wenbin & Lu, Junwei & Zhuang, Guangming & Gao, Fang & Zhang, Zhengqiang & Xu, Shengyuan, 2022. "Further results on stabilization for neutral singular Markovian jump systems with mixed interval time-varying delays," Applied Mathematics and Computation, Elsevier, vol. 420(C).
    11. Zhang, He & Xu, Shengyuan & Zhang, Zhengqiang & Chu, Yuming, 2022. "Practical stability of a nonlinear system with delayed control input," Applied Mathematics and Computation, Elsevier, vol. 423(C).
    12. Dong, Zeyu & Wang, Xin & Zhang, Xian, 2020. "A nonsingular M-matrix-based global exponential stability analysis of higher-order delayed discrete-time Cohen–Grossberg neural networks," Applied Mathematics and Computation, Elsevier, vol. 385(C).
    13. Zhong, Qishui & Han, Sheng & Shi, Kaibo & Zhong, Shouming & Cai, Xiao & Kwon, Oh-Min, 2022. "Distributed secure sampled-data control for distributed generators and energy storage systems in microgrids under abnormal deception attacks," Applied Energy, Elsevier, vol. 326(C).
    14. Shanmugam, Lakshmanan & Joo, Young Hoon, 2023. "Adaptive neural networks-based integral sliding mode control for T-S fuzzy model of delayed nonlinear systems," Applied Mathematics and Computation, Elsevier, vol. 450(C).
    15. Liang, Wei & Zhang, Yongjun & Zhang, Xuanxuan, 2024. "Chaotic behavior of two discrete-time coupled neurons with two delays," Chaos, Solitons & Fractals, Elsevier, vol. 183(C).
    16. Wang, Shengbo & Cao, Yanyi & Huang, Tingwen & Wen, Shiping, 2019. "Passivity and passification of memristive neural networks with leakage term and time-varying delays," Applied Mathematics and Computation, Elsevier, vol. 361(C), pages 294-310.
    17. Han, S.Y. & Kommuri, S.K. & Kwon, O.M. & Lee, S.M., 2022. "Regional sampled-data synchronization of chaotic neural networks using piecewise-continuous delay dependent Lyapunov functional," Applied Mathematics and Computation, Elsevier, vol. 423(C).
    18. Chen, Yonggang & Wang, Zidong & Liu, Yurong & Alsaadi, Fuad E., 2018. "Stochastic stability for distributed delay neural networks via augmented Lyapunov–Krasovskii functionals," Applied Mathematics and Computation, Elsevier, vol. 338(C), pages 869-881.
    19. Zeng, Hong-Bing & Zhai, Zheng-Liang & Wang, Wei, 2021. "Hierarchical stability conditions of systems with time-varying delay," Applied Mathematics and Computation, Elsevier, vol. 404(C).
    20. Vadivel, R. & Hammachukiattikul, P. & Gunasekaran, Nallappan & Saravanakumar, R. & Dutta, Hemen, 2021. "Strict dissipativity synchronization for delayed static neural networks: An event-triggered scheme," Chaos, Solitons & Fractals, Elsevier, vol. 150(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:apmaco:v:438:y:2023:i:c:s0096300322006750. 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: 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.