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 introduced to get higher upper bounds than what traditional inequality produces. Finally, a numerical simulation result is given to validate the advantages of the proposed method for realizing bipartite synchronization.