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Finite-Time and Fixed-Time Synchronization of Memristor-Based Cohen–Grossberg Neural Networks via a Unified Control Strategy

Author

Listed:
  • Mei Liu

    (School of Mathematics and Statistics, Zhoukou Normal University, Zhoukou 466001, China)

  • Binglong Lu

    (School of Mathematics and Statistics, Zhoukou Normal University, Zhoukou 466001, China)

  • Jinling Wang

    (College of Mathematics and Statistics, Northwest Normal University, Lanzhou 730070, China)

  • Haijun Jiang

    (College of Mathematics and System Sciences, Xinjiang University, Urumqi 830046, China)

  • Cheng Hu

    (College of Mathematics and System Sciences, Xinjiang University, Urumqi 830046, China)

Abstract

This article focuses on the problem of finite-time and fixed-time synchronization for Cohen–Grossberg neural networks (CGNNs) with time-varying delays and memristor connection weights. First, through a nonlinear transformation, an alternative system is derived from the Cohen–Grossberg memristor-based neural networks (MCGNNs) considered. Then, under the framework of the Filippov solution and by adjusting a key control parameter, some novel and effective criteria are obtained to ensure finite-time or fixed-time synchronization of the alternative networks via the unified control framework and under the same conditions. Furthermore, the two types of synchronization criteria are derived from the considered MCGNNs. Finally, some numerical simulations are presented to test the validity of these theoretical conclusions.

Suggested Citation

  • Mei Liu & Binglong Lu & Jinling Wang & Haijun Jiang & Cheng Hu, 2025. "Finite-Time and Fixed-Time Synchronization of Memristor-Based Cohen–Grossberg Neural Networks via a Unified Control Strategy," Mathematics, MDPI, vol. 13(4), pages 1-14, February.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:4:p:630-:d:1591494
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