Existence and Exponential Stability of Anti-periodic Solutions for a High-Order Delayed Cohen-Grossberg Neural Networks with Impulsive Effects

被引：17

作者：

Xu, Changjin ^{[1
]}

Zhang, Qiming ^{[2
]}

机构：

[1] Guizhou Univ Finance & Econ, Guizhou Key Lab Econ Syst Simulat, Guiyang 550004, Peoples R China

[2] Hunan Univ Technol, Coll Sci, Zhuzhou 412007, Peoples R China

来源：

NEURAL PROCESSING LETTERS | 2014年 / 40卷 / 03期

基金：

中国国家自然科学基金;

关键词：

High-order Cohen-Grossberg neural networks; Anti-periodic solution; Exponentially stability; Time-varying delay; Impulse; TIME-VARYING DELAYS; CONTINUOUSLY DISTRIBUTED DELAYS; GLOBAL ASYMPTOTIC STABILITY; BOUNDARY-VALUE-PROBLEMS; DIFFERENTIAL-EQUATIONS; LEAKAGE TERM; NEUTRAL-TYPE; SYSTEMS; SCALES;

D O I：

10.1007/s11063-013-9325-6

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, a high-order Cohen-Grossberg neural networks with bounded, unbounded delays and impulses is considered. By using differential inequality techniques, some very verifiable criteria on the existence and exponential stability of anti-periodic solutions for the model are obtained. Our results are new and complementary to previously known results. An example is included to illustrate the feasibility and effectiveness of our main results.

引用

页码：227 / 243

页数：17

共 50 条

[1] Existence and Exponential Stability of Anti-periodic Solutions for a High-Order Delayed Cohen–Grossberg Neural Networks with Impulsive Effects
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[J]. Neural Processing Letters, 2014, 40 : 227 - 243
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