New Results of Finite-Time Synchronization via Piecewise Control for Memristive Cohen-Grossberg Neural Networks With Time-Varying Delays

被引：3
|
作者：
Hui, Meng [1 ]
Luo, Ni [1 ]
Wu, Qisheng [1 ]
Yao, Rui [1 ]
Bai, Lin [1 ]
机构：
[1] Changan Univ, Sch Elect & Control, Xian 710064, Shaanxi, Peoples R China
来源：
IEEE ACCESS | 2019年 / 7卷
基金：
中国国家自然科学基金;
关键词：
Memristive Cohen-Grossberg neural networks; finite-time synchronization; piecewise control; STABILITY ANALYSIS; EXPONENTIAL SYNCHRONIZATION; ADAPTIVE SYNCHRONIZATION; NONLINEAR-SYSTEMS; SAMPLING CONTROL; STABILIZATION;
D O I：
10.1109/ACCESS.2019.2922973
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 presents the finite-time synchronization (FTS) via piecewise control laws for a class of memristive Cohen-Grossberg neural networks (MCGNNs) with time-varying delays. First, based on memristive neural network theory, differential inclusion theory, and stability theory, several new sufficient conditions are established to ensure the FTS stability of a class of MCGNNs with time-varying delays. Then, three control laws are designed. By comparison with a normal control law, the piecewise control law determined by finite-time control (FTC) theta(t) can shorten the settling time. Also, the piecewise control law determined by the dynamic error parallel to epsilon(t)parallel to and FTC theta(t) can shorten the settling time. Finally, a numerical simulation example is provided to illustrate the effectiveness of the new methods.
引用
收藏
页码：79173 / 79185
页数：13
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