Anti-periodic solutions for cellular neural networks with oscillating coefficients in leakage terms

被引：16

作者：

Zhou, Qiyuan ^{[1
]}

机构：

[1] Hunan Univ Arts & Sci, Coll Math & Comp Sci, Changde 415000, Hunan, Peoples R China

来源：

INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS | 2017年 / 8卷 / 05期

关键词：

Cellular neural networks; Anti-periodic solution; Exponential stability; Oscillating coefficient; Leakage term; CONTINUOUSLY DISTRIBUTED DELAYS; EQUATIONS; SYSTEMS;

D O I：

10.1007/s13042-016-0531-1

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper is concerned with the anti-periodic solutions for a class of cellular neural network model with oscillating coefficients in leakage terms. By applying contraction mapping fixed point theorem and differential inequality techniques, we establish some sufficient conditions to guarantee the existence and exponential stability of anti-periodic solutions for this model, which improve and supplement existing ones. Moreover, an example and its numerical simulations are given to support the theoretical results.

引用

页码：1607 / 1613

页数：7

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