Event-Triggered Anti-Synchronization of Fuzzy Delay-Coupled Fractional Memristor-Based Discrete-Time Neural Networks - Chao Wang; Chunlin Gong; Hongtao Yue; Yin Wang (Mathematics, 2025)
This paper investigates the anti-synchronization problem of delay-coupled fractional memristor-based discrete-time neural networks within the T-S fuzzy framework via an event-triggered mechanism. First, fractional-order, coupling topology, and T-S fuzzy rules are incorporated into the discrete-time network model to enhance its applicability. Subsequently, a T-S fuzzy-based event-triggered mechanism is designed, which determines control updates by evaluating whether the system state satisfies predefined triggering conditions, thereby significantly reducing the communication load. Moreover, using diverse fuzzy rules enhances controller flexibility and accuracy. Finally, Zeno behavior is proven to be absent. Using the Lyapunov direct method and inequality techniques, we derive sufficient conditions to ensure anti-synchronization of the proposed system.Numerical simulations confirm the effectiveness of the proposed control scheme and support the theoretical results.