Exponential synchronization of T-S fuzzy complex-valued BAM neural networks with mixed time-varying delays via event-triggered control engineering and applications.

Abstract

This paper investigates the problem of exponential synchronization for Takagi-Sugeno (T-S) fuzzy complex-valued bidirectional associative memory (BAM) neural networks with mixed time-varying delays under an event-triggered control framework. The considered mixed delays, including discrete and distributed time-varying delays, effectively characterize signal transmission and communication phenomena in practical neural network systems. To reduce unnecessary communication and control updates, an event-triggered control strategy is developed, where the control signals are updated only when predefined triggering conditions are satisfied. By constructing an appropriate complex-valued Lyapunov-Krasovskii functional, sufficient conditions for achieving global exponential synchronization are derived in terms of linear matrix inequalities (LMIs). Both static and dynamic event-triggered mechanisms are designed to ensure synchronization performance while significantly reducing communication burden and excluding Zeno behavior. Furthermore, explicit lower bounds for the inter-event intervals are established to guarantee practical implement ability of the proposed control scheme. Finally, numerical simulation results are presented to demonstrate the effectiveness, reliability, and communication efficiency of the proposed method, highlighting its potential applications in complex-valued neural networks and networked control systems.