Non-fragile exponential synchronisation of stochastic neural networks via aperiodic intermittent impulsive control

Abstract

This study aims to address the non-fragile exponential synchronisation problem of stochastic neural networks (SNNs). To cut down unnecessary control costs, a novel aperiodic intermittent-based impulsive control (APIIC) is designed in this investigation. Besides, the randomly occurring gain fluctuation (ROGF) is considered in APIIC, which satisfies certain Bernoulli distributed white noise sequences. By exploiting the Lyapunov approach and the average dwell-time technique, some sufficient criteria are derived in terms of linear matrix inequalities, which ensure that APIIC can achieve exponential synchronisation of SNNs with and without ROGF. More intriguingly, a technical definition of aperiodic window-based average impulsive interval is developed to cut back the conservativeness of these results. At last, the effectiveness of our explored results is confirmed by several numerical examples.