IDEAS home Printed from https://ideas.repec.org/a/eee/apmaco/v424y2022ics0096300322001023.html
   My bibliography  Save this article

Delay-probability-dependent state estimation for neural networks with hybrid delays

Author

Listed:
  • Qian, Wei
  • Liu, Haibo
  • Zhao, Yunji
  • Li, Yalong

Abstract

This dissertation studies the delay-probability-dependent H∞ state estimation issue of neural networks (NNs) with hybrid delays. First, more general system model and state estimator are established by considering discrete delay, distributed delay and probability distribution of time delays. Second, a innovative Lyapunov-Krasovskii functional (LKF) containing augmented non-integral and single-integral quadratic terms is put forward, which can inflect internal connections of multiple functional terms. Meanwhile, in order to handle the infinitesimal operators of LKF effectively, generalized free-weighting-matrix integral inequality (GFWMII) is chosen to cooperate with wirtinger-based inequality. As a consequence, less conservative criteria are obtained, which ensure that the considered system is asymptotically mean-square stable with a desired H∞ performance. Finally, two simulated examples are displayed to bring out the advantage of the achieved approach.

Suggested Citation

  • Qian, Wei & Liu, Haibo & Zhao, Yunji & Li, Yalong, 2022. "Delay-probability-dependent state estimation for neural networks with hybrid delays," Applied Mathematics and Computation, Elsevier, vol. 424(C).
    • Handle: RePEc:eee:apmaco:v:424:y:2022:i:c:s0096300322001023
    DOI: 10.1016/j.amc.2022.127016
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0096300322001023
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.amc.2022.127016?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. 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.
    2. Xie, Wenqian & Zhu, Hong & Zhong, Shouming & Zhang, Dian & Shi, Kaibo & Cheng, Jun, 2018. "Extended dissipative estimator design for uncertain switched delayed neural networks via a novel triple integral inequality," Applied Mathematics and Computation, Elsevier, vol. 335(C), pages 82-102.
    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. Li, Xiaoqing & She, Kun & Zhong, Shouming & Shi, Kaibo & Kang, Wei & Cheng, Jun & Yu, Yongbin, 2018. "Extended robust global exponential stability for uncertain switched memristor-based neural networks with time-varying delays," Applied Mathematics and Computation, Elsevier, vol. 325(C), pages 271-290.
    2. Chen, Jun & Park, Ju H., 2020. "New versions of Bessel–Legendre inequality and their applications to systems with time-varying delay," Applied Mathematics and Computation, Elsevier, vol. 375(C).
    3. Cai, Xiao & Zhong, Shouming & Wang, Jun & Shi, Kaibo, 2020. "Robust H∞ control for uncertain delayed T-S fuzzy systems with stochastic packet dropouts," Applied Mathematics and Computation, Elsevier, vol. 385(C).
    4. Kwon, W. & Koo, Baeyoung & Lee, S.M., 2018. "Novel Lyapunov–Krasovskii functional with delay-dependent matrix for stability of time-varying delay systems," Applied Mathematics and Computation, Elsevier, vol. 320(C), pages 149-157.
    5. Yupeng Shi & Dayong Ye, 2023. "Stability Analysis of Delayed Neural Networks via Composite-Matrix-Based Integral Inequality," Mathematics, MDPI, vol. 11(11), pages 1-13, May.
    6. 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).
    7. Gao, Zhen-Man & He, Yong & Wu, Min, 2019. "Improved stability criteria for the neural networks with time-varying delay via new augmented Lyapunov–Krasovskii functional," Applied Mathematics and Computation, Elsevier, vol. 349(C), pages 258-269.
    8. Gyurkovics, É. & Szabó-Varga, G. & Kiss, K., 2017. "Stability analysis of linear systems with interval time-varying delays utilizing multiple integral inequalities," Applied Mathematics and Computation, Elsevier, vol. 311(C), pages 164-177.
    9. Subramanian, K. & Muthukumar, P. & Lakshmanan, S., 2018. "State feedback synchronization control of impulsive neural networks with mixed delays and linear fractional uncertainties," Applied Mathematics and Computation, Elsevier, vol. 321(C), pages 267-281.
    10. 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).
    11. Zhang, Zhi-Ming & He, Yong & Wu, Min & Wang, Qing-Guo, 2017. "Exponential synchronization of chaotic neural networks with time-varying delay via intermittent output feedback approach," Applied Mathematics and Computation, Elsevier, vol. 314(C), pages 121-132.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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).
    17. 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).
    18. 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).
    19. 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).
    20. 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).

    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:424:y:2022:i:c:s0096300322001023. 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.