Fixed-time synchronization of memristive neural networks with time-varying delays via a new stability criterion

This paper investigates the fixed-time synchronization problem of memristive neural networks with time-varying delays by proposing a novel fixed-time stability theorem. Compared with existing results such as Lemmas 2.3 and 2.4, the proposed theorem provides a tighter upper bound estimate of the settling time, making the calculated convergence time closer to the actual evolution process of the system. Furthermore, the theorem removes the parameter constraints inherent in Lemma 2.5, thereby offering broader applicability. Based on the derived criteria, the influence of power function coefficients on the actual convergence rate is explored in detail. Finally, numerical simulations are provided to demonstrate the effectiveness and superiority of the theoretical results.