Exponential synchronization of stochastic delayed memristive neural networks via a novel hybrid control

• DOI: 10.1016/j.neunet.2020.07.034
• Publication Date: 2020-08-04T15:46:36Z
• Authors: Nijing Yang; Yongbin Yu; Shouming Zhong; Xiangxiang Wang; Kaibo Shi; Jingye Cai
• Keywords: Memristor

This paper investigates the exponential synchronization issue of stochastic delayed memristive neural networks (SDMNNs) via a novel hybrid control (HC), where impulsive instants are determined by the state-dependent trigger condition. The switching and quantification strategies are applied to the event-based impulsive controller to cope with the challenges induced concurrently by interval parameters, impulses, stochastic disturbance and time-varying delays. Furthermore, the control costs can be reduced and communication channels and bandwidths can be saved by using this designed controller. Then, novel Lyapunov functions and new analytical methods are constructed, which can be used to realize the exponential synchronization of SDMNNs via HC. Finally, a numerical simulation is provided to demonstrate our theoretical results.