Fully aperiodic intermittent pinning control for exponential bipartite synchronization of multilayer signed stochastic coupled neural networks

Abstract

This article aims to address the exponential bipartite synchronization (EBS) issue of multilayer signed stochastic coupled neural networks under the fully aperiodic intermittent pinning control (FAIPC) strategy. At first, a network model incorporating stochastic perturbations, multilayer signed graphs and multiple time-varying delays is built. Subsequently, instead of the maximum rest rate condition, by means of the average control rate of aperiodically intermittent control, an improved differential inequality is established to reduce the conservatism of existing results. Furthermore, by employing the presented differential inequality and stochastic analysis technique, some sufficient conditions for ensuring mean square EBS are obtained based on FAIPC with constant control gains and FAIPC with adaptive control gains, respectively. Finally, the availability of the theoretical results is verified by some numerical simulations.