IDEAS home Printed from https://ideas.repec.org/a/eee/apmaco/v410y2021ics0096300321005488.html
   My bibliography  Save this article

Global asymptotic stability and S-asymptotic ω-periodicity of impulsive non-autonomous fractional-order neural networks

Author

Listed:
  • Li, Hui
  • Kao, YongGui
  • Stamova, Ivanka
  • Shao, Chuntao

Abstract

This paper is concerned with the globally S-asymptotic ω-periodicity of impulsive non-autonomous fractional-order (FO) neural networks (NNs). The definition of the piecewise S-asymptotic ω-periodicity of the considered system is given and a new Banach space is constructed. Based on the Banach mapping principle, several novel criteria for the existence and uniqueness of piecewise S-asymptotically ω-periodical solutions are obtained. Then by the FO differential and integral inequalities, we discuss the globally asymptotic stability and globally asymptotic periodicity of the probed FONN. Our methods and results are new. Finally, a numerical example is given to verify the validity of our findings.

Suggested Citation

  • Li, Hui & Kao, YongGui & Stamova, Ivanka & Shao, Chuntao, 2021. "Global asymptotic stability and S-asymptotic ω-periodicity of impulsive non-autonomous fractional-order neural networks," Applied Mathematics and Computation, Elsevier, vol. 410(C).
  • Handle: RePEc:eee:apmaco:v:410:y:2021:i:c:s0096300321005488
    DOI: 10.1016/j.amc.2021.126459
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0096300321005488
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.amc.2021.126459?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, Weiwei & Cao, Jinde & Wu, Ranchao & Chen, Dingyuan & Alsaadi, Fuad E., 2018. "Novel results on projective synchronization of fractional-order neural networks with multiple time delays," Chaos, Solitons & Fractals, Elsevier, vol. 117(C), pages 76-83.
    2. Li, Hong-Li & Zhang, Long & Hu, Cheng & Jiang, Haijun & Cao, Jinde, 2020. "Global Mittag-Leffler synchronization of fractional-order delayed quaternion-valued neural networks: Direct quaternion approach," Applied Mathematics and Computation, Elsevier, vol. 373(C).
    3. Mathiyalagan, Kalidass & Sangeetha, G., 2020. "Second-order sliding mode control for nonlinear fractional-order systems," Applied Mathematics and Computation, Elsevier, vol. 383(C).
    4. Li, Hong-Li & Jiang, Yao-Lin & Wang, Zuolei & Zhang, Long & Teng, Zhidong, 2015. "Global Mittag–Leffler stability of coupled system of fractional-order differential equations on network," Applied Mathematics and Computation, Elsevier, vol. 270(C), pages 269-277.
    5. Wu, Fei & Gao, Renbo & Liu, Jie & Li, Cunbao, 2020. "New fractional variable-order creep model with short memory," Applied Mathematics and Computation, Elsevier, vol. 380(C).
    6. Li, Hui & Kao, YongGui, 2019. "Mittag-Leffler stability for a new coupled system of fractional-order differential equations with impulses," Applied Mathematics and Computation, Elsevier, vol. 361(C), pages 22-31.
    7. Zhang, Jianmei & Wu, Jianwei & Bao, Haibo & Cao, Jinde, 2018. "Synchronization analysis of fractional-order three-neuron BAM neural networks with multiple time delays," Applied Mathematics and Computation, Elsevier, vol. 339(C), pages 441-450.
    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. Pahnehkolaei, Seyed Mehdi Abedi & Alfi, Alireza & Machado, J.A. Tenreiro, 2019. "Delay independent robust stability analysis of delayed fractional quaternion-valued leaky integrator echo state neural networks with QUAD condition," Applied Mathematics and Computation, Elsevier, vol. 359(C), pages 278-293.
    2. Qi, Xingnan & Bao, Haibo & Cao, Jinde, 2019. "Exponential input-to-state stability of quaternion-valued neural networks with time delay," Applied Mathematics and Computation, Elsevier, vol. 358(C), pages 382-393.
    3. Zhen Yang & Zhengqiu Zhang, 2022. "Finite-Time Synchronization Analysis for BAM Neural Networks with Time-Varying Delays by Applying the Maximum-Value Approach with New Inequalities," Mathematics, MDPI, vol. 10(5), pages 1-16, March.
    4. 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).
    5. Jia, You & Wu, Huaiqin & Cao, Jinde, 2020. "Non-fragile robust finite-time synchronization for fractional-order discontinuous complex networks with multi-weights and uncertain couplings under asynchronous switching," Applied Mathematics and Computation, Elsevier, vol. 370(C).
    6. Zhang, Weiwei & Zhang, Hai & Cao, Jinde & Zhang, Hongmei & Chen, Dingyuan, 2020. "Synchronization of delayed fractional-order complex-valued neural networks with leakage delay," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 556(C).
    7. Syed Ali, M. & Narayanan, Govindasamy & Shekher, Vineet & Alsulami, Hamed & Saeed, Tareq, 2020. "Dynamic stability analysis of stochastic fractional-order memristor fuzzy BAM neural networks with delay and leakage terms," Applied Mathematics and Computation, Elsevier, vol. 369(C).
    8. 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.
    9. Du, Feifei & Lu, Jun-Guo, 2021. "New criterion for finite-time synchronization of fractional order memristor-based neural networks with time delay," Applied Mathematics and Computation, Elsevier, vol. 389(C).
    10. Li, Hui & Kao, Yonggui & Li, Hong-Li, 2021. "Globally β-Mittag-Leffler stability and β-Mittag-Leffler convergence in Lagrange sense for impulsive fractional-order complex-valued neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 148(C).
    11. Li, Hong-Li & Kao, Yonggui & Hu, Cheng & Jiang, Haijun & Jiang, Yao-Lin, 2021. "Robust exponential stability of fractional-order coupled quaternion-valued neural networks with parametric uncertainties and impulsive effects," Chaos, Solitons & Fractals, Elsevier, vol. 143(C).
    12. Hou, Mimi & Xi, Xuan-Xuan & Zhou, Xian-Feng, 2021. "Boundary control of a fractional reaction-diffusion equation coupled with fractional ordinary differential equations with delay," Applied Mathematics and Computation, Elsevier, vol. 406(C).
    13. Duc, Tran Minh & Van Hoa, Ngo, 2021. "Stabilization of impulsive fractional-order dynamic systems involving the Caputo fractional derivative of variable-order via a linear feedback controller," Chaos, Solitons & Fractals, Elsevier, vol. 153(P2).
    14. Ren, Jing & Zhai, Chengbo, 2020. "Stability analysis for generalized fractional differential systems and applications," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    15. Yang, Xujun & Li, Chuandong & Huang, Tingwen & Song, Qiankun, 2017. "Mittag–Leffler stability analysis of nonlinear fractional-order systems with impulses," Applied Mathematics and Computation, Elsevier, vol. 293(C), pages 416-422.
    16. Zhao, Mingfang & Li, Hong-Li & Zhang, Long & Hu, Cheng & Jiang, Haijun, 2023. "Quasi-synchronization of discrete-time fractional-order quaternion-valued memristive neural networks with time delays and uncertain parameters," Applied Mathematics and Computation, Elsevier, vol. 453(C).
    17. Shu, Jinlong & Wu, Baowei & Xiong, Lianglin, 2022. "Stochastic stability criteria and event-triggered control of delayed Markovian jump quaternion-valued neural networks," Applied Mathematics and Computation, Elsevier, vol. 420(C).
    18. Zheng, Bibo & Wang, Zhanshan, 2022. "Mittag-Leffler synchronization of fractional-order coupled neural networks with mixed delays," Applied Mathematics and Computation, Elsevier, vol. 430(C).
    19. Mei-Qi, Wang & Wen-Li, Ma & En-Li, Chen & Yu-Jian, Chang & Cui-Yan, Wang, 2022. "Principal resonance analysis of piecewise nonlinear oscillator with fractional calculus," Chaos, Solitons & Fractals, Elsevier, vol. 154(C).
    20. Wang, Haitao & Chen, Xiangyong & Wang, Jing, 2022. "H∞ sliding mode control for PDT-switched nonlinear systems under the dynamic event-triggered mechanism," Applied Mathematics and Computation, Elsevier, vol. 412(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:410:y:2021:i:c:s0096300321005488. 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.