Anti-periodic Solutions of Inertial Neural Networks with Time Delays

被引：50
|
作者：
Ke, Yunquan [1 ]
Miao, Chunfang [1 ]
机构：
[1] Shaoxing Univ, Dept Math, Shaoxing, Zhejiang, Peoples R China
来源：
NEURAL PROCESSING LETTERS | 2017年 / 45卷 / 02期
关键词：
Inertial term; Neural networks; Lyapunov method; Anti-periodic solutions; Exponential stability; PERIODIC-SOLUTIONS; STABILITY; EXISTENCE; MODEL; SYNCHRONIZATION; CHAOS;
D O I：
10.1007/s11063-016-9540-z
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, the exponential stability of anti-periodic solutions for inertial neural networks with time delays is investigated. First, by properly chosen variable substitution the system is transformed to first order differential equation. Second, some sufficient conditions which can ensure the existence and exponential stability of anti-periodic solutions for the system are obtained by using Lyapunov method and uniformly converges. Finally, an example is given to illustrate the effectiveness of the results.
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页码：523 / 538
页数：16
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