IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v8y2020i7p1146-d383826.html
   My bibliography  Save this article

Finite-Time Mittag–Leffler Synchronization of Neutral-Type Fractional-Order Neural Networks with Leakage Delay and Time-Varying Delays

Author

Listed:
  • Călin-Adrian Popa

    (Department of Mathematics, West University of Timişoara, Blvd. V. Pârvan, No. 4, 300223 Timişoara, Romania
    Department of Computer and Software Engineering, Polytechnic University Timişoara, Blvd. V. Pârvan, No. 2, 300223 Timişoara, Romania
    These authors contributed equally to this work.)

  • Eva Kaslik

    (Department of Computer Science, West University of Timişoara, Blvd. V. Pârvan, No. 4, 300223 Timişoara, Romania
    These authors contributed equally to this work.)

Abstract

This paper studies fractional-order neural networks with neutral-type delay, leakage delay, and time-varying delays. A sufficient condition which ensures the finite-time synchronization of these networks based on a state feedback control scheme is deduced using the generalized Gronwall–Bellman inequality. Then, a different state feedback control scheme is employed to realize the finite-time Mittag–Leffler synchronization of these networks by using the fractional-order extension of the Lyapunov direct method for Mittag–Leffler stability. Two numerical examples illustrate the feasibility and the effectiveness of the deduced sufficient criteria.

Suggested Citation

  • Călin-Adrian Popa & Eva Kaslik, 2020. "Finite-Time Mittag–Leffler Synchronization of Neutral-Type Fractional-Order Neural Networks with Leakage Delay and Time-Varying Delays," Mathematics, MDPI, vol. 8(7), pages 1-17, July.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:7:p:1146-:d:383826
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/8/7/1146/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/8/7/1146/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yuan, Jun & Zhao, Lingzhi & Huang, Chengdai & Xiao, Min, 2019. "Novel results on bifurcation for a fractional-order complex-valued neural network with leakage delay," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 868-883.
    2. Huang, Chengdai & Meng, Yijie & Cao, Jinde & Alsaedi, Ahmed & Alsaadi, Fuad E., 2017. "New bifurcation results for fractional BAM neural network with leakage delay," Chaos, Solitons & Fractals, Elsevier, vol. 100(C), pages 31-44.
    3. Zhang, Yanlin & Deng, Shengfu, 2019. "Finite-time projective synchronization of fractional-order complex-valued memristor-based neural networks with delay," Chaos, Solitons & Fractals, Elsevier, vol. 128(C), pages 176-190.
    4. Hu, Taotao & He, Zheng & Zhang, Xiaojun & Zhong, Shouming, 2020. "Finite-time stability for fractional-order complex-valued neural networks with time delay," Applied Mathematics and Computation, Elsevier, vol. 365(C).
    5. Zhang, Lei & Song, Qiankun & Zhao, Zhenjiang, 2017. "Stability analysis of fractional-order complex-valued neural networks with both leakage and discrete delays," Applied Mathematics and Computation, Elsevier, vol. 298(C), pages 296-309.
    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. Jiashu Dai & Chengdong Yang, 2022. "Synchronization of Nonlinear Complex Spatiotemporal Networks Based on PIDEs with Multiple Time Delays: A P-sD Method," Mathematics, MDPI, vol. 10(3), pages 1-15, February.
    2. Pengfei Guo & Yunong Zhang, 2022. "Tracking Control for Triple-Integrator and Quintuple-Integrator Systems with Single Input Using Zhang Neural Network with Time Delay Caused by Backward Finite-Divided Difference Formulas for Multiple-," Mathematics, MDPI, vol. 10(9), pages 1-27, April.

    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. Xu, Changjin & Liu, Zixin & Liao, Maoxin & Li, Peiluan & Xiao, Qimei & Yuan, Shuai, 2021. "Fractional-order bidirectional associate memory (BAM) neural networks with multiple delays: The case of Hopf bifurcation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 182(C), pages 471-494.
    2. Xu, Changjin & Liu, Zixin & Yao, Lingyun & Aouiti, Chaouki, 2021. "Further exploration on bifurcation of fractional-order six-neuron bi-directional associative memory neural networks with multi-delays," Applied Mathematics and Computation, Elsevier, vol. 410(C).
    3. Yuan, Jun & Zhao, Lingzhi & Huang, Chengdai & Xiao, Min, 2019. "Novel results on bifurcation for a fractional-order complex-valued neural network with leakage delay," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 868-883.
    4. 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).
    5. Yang, Zhanying & Zhang, Jie & Zhang, Zhihui & Mei, Jun, 2023. "An improved criterion on finite-time stability for fractional-order fuzzy cellular neural networks involving leakage and discrete delays," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 203(C), pages 910-925.
    6. 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).
    7. Tu, Zhengwen & Ding, Nan & Li, Liangliang & Feng, Yuming & Zou, Limin & Zhang, Wei, 2017. "Adaptive synchronization of memristive neural networks with time-varying delays and reaction–diffusion term," Applied Mathematics and Computation, Elsevier, vol. 311(C), pages 118-128.
    8. Udhayakumar, K. & Rakkiyappan, R. & Li, Xiaodi & Cao, Jinde, 2021. "Mutiple ψ-type stability of fractional-order quaternion-valued neural networks," Applied Mathematics and Computation, Elsevier, vol. 401(C).
    9. Tu, Zhengwen & Zhao, Yongxiang & Ding, Nan & Feng, Yuming & Zhang, Wei, 2019. "Stability analysis of quaternion-valued neural networks with both discrete and distributed delays," Applied Mathematics and Computation, Elsevier, vol. 343(C), pages 342-353.
    10. Li, Mingyue & Chen, Huanzhen & Li, Xiaodi, 2021. "Exponential stability of nonlinear systems involving partial unmeasurable states via impulsive control," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    11. Tan, Lihua & Li, Chuandong & Huang, Junjian & Huang, Tingwen, 2021. "Output feedback leader-following consensus for nonlinear stochastic multiagent systems: The event-triggered method," Applied Mathematics and Computation, Elsevier, vol. 395(C).
    12. He, Jin-Man & Pei, Li-Jun, 2023. "Function matrix projection synchronization for the multi-time delayed fractional order memristor-based neural networks with parameter uncertainty," Applied Mathematics and Computation, Elsevier, vol. 454(C).
    13. Oliveira, José J., 2022. "Global stability criteria for nonlinear differential systems with infinite delay and applications to BAM neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).
    14. Zhixin Zhang & Yufeng Zhang & Jia-Bao Liu & Jiang Wei, 2019. "Global Asymptotical Stability Analysis for Fractional Neural Networks with Time-Varying Delays," Mathematics, MDPI, vol. 7(2), pages 1-8, February.
    15. Panda, Sumati Kumari & Nagy, A.M. & Vijayakumar, Velusamy & Hazarika, Bipan, 2023. "Stability analysis for complex-valued neural networks with fractional order," Chaos, Solitons & Fractals, Elsevier, vol. 175(P2).
    16. Shuai Li & Chengdai Huang & Xinyu Song, 2019. "Bifurcation Based-Delay Feedback Control Strategy for a Fractional-Order Two-Prey One-Predator System," Complexity, Hindawi, vol. 2019, pages 1-13, April.
    17. Lu, Xiaoling & Sun, Weihua, 2024. "Control and synchronization of Julia sets of discrete fractional Ising models," Chaos, Solitons & Fractals, Elsevier, vol. 180(C).
    18. Shuang Wang & Hai Zhang & Weiwei Zhang & Hongmei Zhang, 2021. "Finite-Time Projective Synchronization of Caputo Type Fractional Complex-Valued Delayed Neural Networks," Mathematics, MDPI, vol. 9(12), pages 1-14, June.
    19. Mo, Lipo & Yuan, Xiaolin & Yu, Yongguang, 2018. "Target-encirclement control of fractional-order multi-agent systems with a leader," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 479-491.
    20. Jin-E Zhang, 2017. "Multisynchronization for Coupled Multistable Fractional-Order Neural Networks via Impulsive Control," Complexity, Hindawi, vol. 2017, pages 1-10, August.

    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:8:y:2020:i:7:p:1146-:d:383826. 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.