Synchronization of short memory fractional coupled neural networks with higher-order interactions via novel intermittent control

Abstract

Due to the fact that higher-order interactions in neural networks significantly enhance the accuracy and depth of network modeling and analysis, this paper investigates the synchronization problem in such networks by employing a novel intermittent control method. Firstly, higher-order interactions in the fractional coupled neural network model are considered, extending the traditional understanding of neural network structures. Based on a designed threshold function, a flexible intermittent controller is introduced. Furthermore, sufficient conditions for achieving network synchronization are provided, ensuring the network reaches a synchronized state under the proposed control method. Alongside these conditions, synchronization criteria are presented to strictly control the synchronization error within a predetermined decay range, guaranteeing the performance meets specific accuracy requirements. Finally, the effectiveness of our innovative intermittent control strategy is demonstrated through two numerical simulations.