Anti-periodic solution for fuzzy Cohen-Grossberg neural networks with time-varying and distributed delays

被引：2

作者：

Li, Yongkun ^{[1
]}

Yang, Li ^{[1
]}

Wu, Wanqin ^{[2
]}

机构：

[1] Yunnan Univ, Dept Math, Kunming 650091, Yunnan, Peoples R China

[2] Yunnan Nationalities Univ, Sch Math & Comp Sci, Kunming 650091, Yunnan, Peoples R China

来源：

NONLINEAR ANALYSIS-MODELLING AND CONTROL | 2015年 / 20卷 / 03期

关键词：

Cohen-Grossberg neural networks; exponential stability; anti-periodic solutions; coincidence degree; GLOBAL EXPONENTIAL STABILITY; VARIABLE-COEFFICIENTS; ASYMPTOTIC STABILITY; PERIODIC-SOLUTIONS; IMPULSES; DISCRETE; INTERPOLATION; EXISTENCE; EQUATIONS; SCALES;

D O I：

10.15388/NA.2015.3.6

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

In this paper, by using a continuation theorem of coincidence degree theory and a differential inequality, we establish some sufficient conditions ensuring the existence and global exponential stability of anti-periodic solutions for a class of fuzzy Cohen-Grossberg neural networks with time-varying and distributed delays. In addition, we present an illustrative example to show the feasibility of obtained results.

引用

页码：395 / 416

页数：22

共 41 条

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