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

Mei Liu (), Binglong Lu, Jinling Wang, Haijun Jiang and Cheng Hu
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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

Mathematics, 2025, vol. 13, issue 4, 1-14

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.

Keywords: memristor; fixed-time synchronization; finite-time synchronization; Cohen–Grossberg neural network (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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