Event-triggered bipartite synchronization of coupled multi-order fractional neural networks

This paper addresses the bipartite synchronization of coupled multi-order fractional neural networks (MFNNs) with time-varying delays. An effective event-triggered controller is proposed, and sufficient criteria for ensuring the bipartite synchronization are derived by using the Lyapunov function in vector form and the comparison principle for multi-order fractional differential equations. In addition, the preclusion of Zeno behavior is discussed. The results obtained in this paper cover the bipartite synchronization of both fractional neural networks with identical order and integer-order neural networks as special cases. A numerical example is given to verify the effectiveness of the proposed results.