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Bipartite Synchronization of Fractional-Order Memristor-Based Coupled Delayed Neural Networks with Pinning Control

Author

Listed:
  • P. Babu Dhivakaran

    (Department of Mathematics, Amrita School of Physical Sciences, Amrita Vishwa Vidyapeetham, Coimbatore 641112, India)

  • A. Vinodkumar

    (Department of Mathematics, Amrita School of Physical Sciences, Amrita Vishwa Vidyapeetham, Coimbatore 641112, India)

  • S. Vijay

    (Department of Mathematics, Government Arts College, Udhagamandalam 643002, India)

  • S. Lakshmanan

    (Division of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Chennai 600127, India)

  • J. Alzabut

    (Department of Mathematics and General Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia
    Department of Industrial Engineering, OSTİM Technical University, 06374 Ankara, Türkiye)

  • R. A. El-Nabulsi

    (Research Center for Quantum Technology, Faculty of Science, Chiang Mai University, Chiang Mai 50200, Thailand
    Department of Physics and Materials Science, Faculty of Science, Caiang Mai University, Caiang Mai 50200, Thailand
    Athens Institute for Education and Research, Mathematics and Physics Divisions, 8 Valaoritou Street, Kolonaki, 10671 Athens, Greece)

  • W. Anukool

    (Research Center for Quantum Technology, Faculty of Science, Chiang Mai University, Chiang Mai 50200, Thailand
    Department of Physics and Materials Science, Faculty of Science, Caiang Mai University, Caiang Mai 50200, Thailand)

Abstract

This paper investigates the bipartite synchronization of memristor-based fractional-order coupled delayed neural networks with structurally balanced and unbalanced concepts. The main result is established for the proposed model using pinning control, fractional-order Jensen’s inequality, and the linear matrix inequality. Further, new sufficient conditions are derived using the Lyapunov–Krasovskii functional with delay-dependent criteria. Finally, numerical simulations are provided including two numerical examples to show the effectiveness of the theoretical results.

Suggested Citation

  • P. Babu Dhivakaran & A. Vinodkumar & S. Vijay & S. Lakshmanan & J. Alzabut & R. A. El-Nabulsi & W. Anukool, 2022. "Bipartite Synchronization of Fractional-Order Memristor-Based Coupled Delayed Neural Networks with Pinning Control," Mathematics, MDPI, vol. 10(19), pages 1-13, October.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:19:p:3699-:d:937388
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    Cited by:

    1. Sundarapandian Vaidyanathan & Ahmad Taher Azar & Ibrahim A. Hameed & Khaled Benkouider & Esteban Tlelo-Cuautle & Brisbane Ovilla-Martinez & Chang-Hua Lien & Aceng Sambas, 2023. "Bifurcation Analysis, Synchronization and FPGA Implementation of a New 3-D Jerk System with a Stable Equilibrium," Mathematics, MDPI, vol. 11(12), pages 1-22, June.

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