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

被引：0

作者：

Zhou L.-Q. ^{[1
]}

Song X.-H. ^{[1
]}

机构：

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

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：

Impulse effect; Lyapunov functional; Polynomial stability; Proportional delays; Recurrent neural networks;

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

页数：9

共 32 条

[1]

WU B, LIU Y, LU J., New results on global exponential stability for impulsive cellular neural networks with any bounded time-varying delays, Mathematical and Computer Modeling, 55, 3-4, pp. 837-843, (2012)

[2]

WANG X, LI C, HUANG T, Et al., Global exponential stability of a class of memristive neural networks with time-varying delays, Neural Computing & Applications, 24, 7-8, pp. 1707-1715, (2014)

[3]

LIU X, LIU X, TANG M, Et al., Improved exponential stability criterion for neural networks with time-varying delay, Neurocomputing, 234, pp. 154-163, (2017)

[4]

ZHANG G, HU J, JIANG F., Show more Exponential stability criteria for delayed second-order memristive neural networks, Neurocomputing, 315, pp. 439-446, (2018)

[5]

ZHANG X, HAN Q., Global asymptotic stability analysis for delayed neural networks using a matrix-based quadratic convex approach, Neural Networks, 54, pp. 57-69, (2014)

[6]

ALI M S, SARAVANAN S, RANI M E, Et al., Asymptotic stability of Cohen-Grossberg BAM neutral type neural networks with distributed time varying delays, Neural Processing Letters, 46, 3, pp. 991-1007, (2017)

[7]

WANG L, DING X, LI M., Global asymptotic stability of a class of generalized BAM neural networks with reaction- diffusion terms and mixed time delays, Neurocomputing, 321, pp. 251-265, (2018)

[8]

SANTOS M L, ROCHA M P C, GOMES S C., Polynomial stability of a coupled system of wave equations weakly dissipative, Applicable Analysis, 86, 10, pp. 1293-1302, (2010)

[9]

LIU Z, RAO B., Frequency domain approach for the polynomial stability of a system of partially damped wave equations, Journal of Mathematical Analysis and Applications, 335, 2, pp. 860-88115, (2007)

[10]

MAO X., Almost sure polynomial stability for a class of stochastic differential equations, Quarterly Journal of Mathematics, 43, 3, pp. 339-348, (1992)

← 1 2 3 4 →