Bipartite synchronization for cooperative-competitive neural networks with reaction-diffusion terms via dual event-triggered mechanism

The pinning-like bipartite synchronization is investigated for reaction-diffusion neural networks with cooperative-competitive interactions in this paper. First, a dural event-triggered control algorithm based on the time-space sampled-data scheme is employed to further decrease the transmission resources' consumption. Then, some sufficient conditions that guarantee the bipartite synchronization for the target neural networks with the signed graph are obtained by virtue of the Lyapunov method, Halanay's inequalities, and the pinning control technique. Moreover, new weighted integral inequalities are intro-duced to get higher upper bounds than what traditional inequality produces. Finally, a numerical simu-lation result is given to validate the advantages of the proposed method for realizing bipartite synchronization.& COPY; 2023 Elsevier B.V. All rights reserved.

Automated summary

In this paper, the pinning-like bipartite synchronization is investigated for reaction-diffusion neural networks with cooperative-competitive interactions. A dual event-triggered control algorithm based on the time-space sampled-data scheme is employed to reduce transmission resources' consumption. Sufficient conditions for bipartite synchronization of target neural networks with signed graphs are obtained using the Lyapunov method, Halanay's inequalities, and the pinning control technique. New weighted integral inequalities are introduced to obtain higher upper bounds than traditional inequalities. A numerical simulation result is provided to validate the advantages of the proposed method for achieving bipartite synchronization.