IDEAS home Printed from https://ideas.repec.org/a/eee/apmaco/v442y2023ics0096300322008293.html
   My bibliography  Save this article

Observer-based sliding mode synchronization control of complex-valued neural networks with inertial term and mixed time-varying delays

Author

Listed:
  • Guo, Runan
  • Xu, Shengyuan

Abstract

In this paper, the synchronization problem of complex-valued inertial neural networks is studied via sliding mode control (SMC). Both mixed time-varying delays and unknown control disturbances are considered. In the absence of the equivalent transformations of real- and complex-valued systems, the systems are analyzed as an entirety form in complex domain. A disturbance observer is designed to estimate the unknown control disturbance. By constructing suitable integral switching surface function and innovative Lyapunov–Krasovskii functionals, a delay-dependent synchronization criterion is proposed on the basis of linear matrix inequality technique. An efficient SMC law based on the disturbance observer is designed, and the accessibility analysis of the predefined switching surface is provided. Eventually, numerical verification based on two types of activation functions, as well as the superiority and practicability analysis are provided.

Suggested Citation

  • 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).
  • Handle: RePEc:eee:apmaco:v:442:y:2023:i:c:s0096300322008293
    DOI: 10.1016/j.amc.2022.127761
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0096300322008293
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.amc.2022.127761?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. 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.
    2. 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).
    3. Zhu, Sha & Bao, Haibo, 2022. "Event-triggered synchronization of coupled memristive neural networks," Applied Mathematics and Computation, Elsevier, vol. 415(C).
    4. Wei, Xiaofeng & Zhang, Ziye & Lin, Chong & Chen, Jian, 2021. "Synchronization and anti-synchronization for complex-valued inertial neural networks with time-varying delays," Applied Mathematics and Computation, Elsevier, vol. 403(C).
    5. Long, Changqing & Zhang, Guodong & Hu, Junhao, 2021. "Fixed-time synchronization for delayed inertial complex-valued neural networks," Applied Mathematics and Computation, Elsevier, vol. 405(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. Cheng, Guifang & Liu, Hao, 2024. "Asynchronous finite-time extended dissipative sliding mode control for semi-Markovian jump master–slave neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 179(C).

    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. Abdurahman, Abdujelil & Abudusaimaiti, Mairemunisa & Jiang, Haijun, 2023. "Fixed/predefined-time lag synchronization of complex-valued BAM neural networks with stochastic perturbations," Applied Mathematics and Computation, Elsevier, vol. 444(C).
    2. Kumar, Ankit & Das, Subir & Singh, Sunny & Rajeev,, 2023. "Quasi-projective synchronization of inertial complex-valued recurrent neural networks with mixed time-varying delay and mismatched parameters," Chaos, Solitons & Fractals, Elsevier, vol. 166(C).
    3. Zheng, Yi & Wu, Xiaoqun & Fan, Ziye & Wang, Wei, 2022. "Identifying topology and system parameters of fractional-order complex dynamical networks," Applied Mathematics and Computation, Elsevier, vol. 414(C).
    4. 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.
    5. Lin Cao & Rongwei Guo, 2022. "Partial Anti-Synchronization Problem of the 4D Financial Hyper-Chaotic System with Periodically External Disturbance," Mathematics, MDPI, vol. 10(18), pages 1-14, September.
    6. Sun, Meng & Zhuang, Guangming & Xia, Jianwei & Wang, Yanqian & Chen, Guoliang, 2022. "Stochastic admissibility and H∞ output feedback control for singular Markov jump systems under dynamic measurement output event-triggered strategy," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).
    7. Yang, Wei & Cui, Guozeng & Ma, Qian & Ma, Jiali & Tao, Chongben, 2022. "Finite-time adaptive event-triggered command filtered backstepping control for a QUAV," Applied Mathematics and Computation, Elsevier, vol. 423(C).
    8. Liu, Haoliang & Zhang, Taixiang & Li, Xiaodi, 2021. "Event-triggered control for nonlinear systems with impulse effects," Chaos, Solitons & Fractals, Elsevier, vol. 153(P1).
    9. Chen, Wei & Yu, Yongguang & Hai, Xudong & Ren, Guojian, 2022. "Adaptive quasi-synchronization control of heterogeneous fractional-order coupled neural networks with reaction-diffusion," Applied Mathematics and Computation, Elsevier, vol. 427(C).
    10. Baluni, Sapna & Sehgal, Ishani & Yadav, Vijay K. & Das, Subir, 2024. "Exponential synchronization of a class of quaternion-valued neural network with time-varying delays: A Matrix Measure Approach," Chaos, Solitons & Fractals, Elsevier, vol. 182(C).
    11. 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.
    12. Wenjun Dong & Yujiao Huang & Tingan Chen & Xinggang Fan & Haixia Long, 2022. "Local Lagrange Exponential Stability Analysis of Quaternion-Valued Neural Networks with Time Delays," Mathematics, MDPI, vol. 10(13), pages 1-21, June.
    13. 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.
    14. Luo, Mei & Wang, JinRong & Meng, Deyuan, 2023. "Stochastic convergence problems on switching networks: An event-triggered method," Chaos, Solitons & Fractals, Elsevier, vol. 170(C).
    15. Chang, Wenting & Sang, Hong & Guo, Liangdong & Wu, Libing & Dimirovski, Georgi M., 2024. "Integrated L∞ anti-disturbance synchronization control for switched neural networks with unknown delays," Chaos, Solitons & Fractals, Elsevier, vol. 179(C).
    16. Kowsalya, P. & Mohanrasu, S.S. & Kashkynbayev, Ardak & Gokul, P. & Rakkiyappan, R., 2024. "Fixed-time synchronization of Inertial Cohen-Grossberg Neural Networks with state dependent delayed impulse control and its application to multi-image encryption," Chaos, Solitons & Fractals, Elsevier, vol. 181(C).
    17. Yuan, Manman & Luo, Xiong & Mao, Xue & Han, Zhen & Sun, Lei & Zhu, Peican, 2022. "Event-triggered hybrid impulsive control on lag synchronization of delayed memristor-based bidirectional associative memory neural networks for image hiding," Chaos, Solitons & Fractals, Elsevier, vol. 161(C).
    18. Ganesan, Bhuvaneshwari & Annamalai, Manivannan, 2023. "Anti-synchronization analysis of chaotic neural networks using delay product type looped-Lyapunov functional," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).
    19. Md Sayeed Anwar & Dibakar Ghosh & Nikita Frolov, 2021. "Relay Synchronization in a Weighted Triplex Network," Mathematics, MDPI, vol. 9(17), pages 1-10, September.
    20. Shi, Sangli & Wang, Zhengxin & Song, Qiang & Xiao, Min & Jiang, Guo-Ping, 2022. "Leader-following quasi-bipartite synchronization of coupled heterogeneous harmonic oscillators via event-triggered control," Applied Mathematics and Computation, Elsevier, vol. 427(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:442:y:2023:i:c:s0096300322008293. 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.