Reachable Set Estimation for a Class of Memristor-Based Neural Networks With Time-Varying Delays

被引：7
|
作者：
Zhao, Jiemei [1 ]
机构：
[1] Wuhan Polytech Univ, Sch Math & Comp Sci, Wuhan 430023, Hubei, Peoples R China
来源：
IEEE ACCESS | 2018年 / 6卷
关键词：
Memristor-based neural networks; reachable set estimation; time-varying delays; linear matrix inequality; DIFFERENT MEMDUCTANCE FUNCTIONS; BOUNDED PEAK INPUTS; LINEAR-SYSTEMS; EXPONENTIAL STABILITY; SYNCHRONIZATION CRITERIA; SINGULAR SYSTEMS; DISTURBANCES; CONTROLLER; PASSIVITY; DESIGN;
D O I：
10.1109/ACCESS.2017.2777008
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper investigates the reachable set estimation problem for a class of memristor-based neural networks with time-varying delays and bounded disturbances. By constructing a Lyapunov-Krasovskii functional, a sufficient condition for the solvability of the addressed problem is established based on linear matrix inequality. This condition ensuring the existence of an ellipsoid that contains all the states under initial conditions. A stability criterion of memristor-based neural networks with time varying delays is also given. Two numerical examples are provided to show the effectiveness of the proposed methods.
引用
收藏
页码：937 / 943
页数：7
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