Exponential Stability of Markovian Jumping Memristor-Based Neural Networks via Event-Triggered Impulsive Control Scheme

Abstract

This paper studies the modeling and exponential stability problems for markovian jumping memristor-based neural networks (MJMNNs) via event-triggered impulsive control scheme (ETICS). The purpose is to design memristor-based neural networks (MNNs) which has markovian jumping parameters and hybrid time-vary delays to make the MNNs more general. Meanwhile, a state estimator is introduced to estimate system states through a vailable output measurements. Furthermore, the proposed event-triggered scheme (ETS), which is also determined by markovian parameters, is used to determine whether there is an impulse and whether the system need to transmit the sampled state information. Then, by using Lyapunov-Krasovskii functional (LKF) and an improved inequality, exponential stable criterions are established. Finally, a numerical example is given to support the results.

Keywords

• Memristor-based neural networks
• Markovian jumping
• event-triggered impulsive control scheme
• exponential stability