Fixed-time bipartite synchronization of coopetition memristive neural networks: Parameter optimization of event-triggered switching controller

Abstract

This paper addresses fixed-time bipartite synchronization in memristive neural networks with cooperation and competition interactions. For the purpose of establishing an easily analyzable error system model, the interval matrix techniques are utilized to overcome the impact of memristive connection weights, while signed graph theory and coordinate transformation methods are employed to handle cooperation and competition interactions. An event-triggered switching controller without Zeno behavior is designed aiming to achieve the fixed-time bipartite synchronization while conserving network bandwidth. Then, the fixed-time bipartite synchronization criteria for coopetition memristive neural networks are obtained via Lyapunov stability theory and inequality techniques. Leveraging the sparrow search algorithm and fixed-time bipartite synchronization criteria, the solution algorithm for optimizing the control parameters is established to minimize the settling time’s upper bound. Simulations confirm the control strategy’s efficacy and performance advantages in solving fixed-time bipartite synchronization.