Exponential Synchronization of Memristive Neural Networks with Discrete and Distributed Time-Varying Delays via Event-Triggered Control

Biwen Li,Wenbo Zhou,Shiping Wen

doi:10.1155/2021/5575849

Abstract

In this paper, we investigate the exponential synchronization problem of memristive neural networks (MNNs) with discrete and distributed time-varying delays under event-triggered control. An event-triggered controller with the static and dynamic event-triggering conditions is designed to improve the efficiency of resource utilization. By constructing a new Lyapunov function, some sufficient criteria are obtained to realize the exponential synchronization of considered drive-response MNNs under the designed event-triggered controller. In addition, the Zeno behavior will not occur by proving that the event-triggering interval has a positive lower bound under different event-triggering conditions. Finally, a numerical example is provided to prove the validity of our theoretical results.

Highlights

Memristor, as the fourth fundamental circuit element, was predicted to exist by Chua [1] in 1971
Static and dynamic event-triggering functions will be presented to ensure that the event-triggered exponential synchronization between drive memristive neural networks (MNNs) (1) and response MNNs (3) with discrete and distributed time-varying delays can be realized
Based on the drive-response concept, the synchronization error system has been derived. en, an event-triggered controller with static and dynamic event-triggering conditions has been designed to improve the efficiency of resource utilization

Summary

Introduction

As the fourth fundamental circuit element, was predicted to exist by Chua [1] in 1971. The physical implementation of memristor was very difficult by the immature nanotechnology at that time. It was not until 2008 that the prototype of memristor was manufactured firstly by the Hewlett-Packard research team [2, 3]. E stability of neural networks as a crucial prerequisite to the application is very important, and the problem for stability analysis of MNNs has been widely studied by many researchers [5,6,7,8,9,10,11,12]. Neural networks generally have a spatial extent due to the existence of a large number of parallel pathways with different lengths and sizes of axons

Objectives

Results

Conclusion