Global Polynomial Stability of a Class of Impulsive Recurrent Neural Networks with Proportional Delays

被引：0

作者：

Zhou, Li-Qun ^{[1
]}

Song, Xie-Hui ^{[1
]}

机构：

[1] School of Mathematics Science, Tianjin Normal University, Tianjin,300387, China

来源：

Dianzi Keji Daxue Xuebao/Journal of the University of Electronic Science and Technology of China | 2021年 / 50卷 / 01期

关键词：

D O I：

10.12178/1001-0548.2019171

中图分类号：

学科分类号：

摘要：

The definition of global polynomial stability for a class of impulsive recurrent neural networks (IRNNs) with proportional delays is given. By introducing adjustable parameters, several suitable Lyapunov functionals are constructed and the method of linear matrix inequality (LMI) is used to discuss the global polynomial stability of the system. Several criteria for guaranteeing the global polynomial stability of the system are obtained. And these criteria are given in the form of LMI, which is convenient to use Matlab toolbox for verification. The relationship between polynomial stability and exponential stability is revealed. The criteria are verified by numerical examples. Copyright ©2021 Journal of University of Electronic Science and Technology of China. All rights reserved.

引用

页码：91 / 100

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