Global exponential periodicity and stability of recurrent neural networks with multi-proportional delays

被引：28
|
作者：
Zhou, Liqun [1 ]
Zhang, Yanyan [1 ]
机构：
[1] Tianjin Normal Univ, Sch Math Sci, Tianjin 300387, Peoples R China
来源：
ISA TRANSACTIONS | 2016年 / 60卷
基金：
美国国家科学基金会;
关键词：
Recurrent neural networks (RNNs); Proportional delay factor; Global exponential periodicity; Global exponential stability; Nonlinear transformation; ROBUST STABILITY; ASYMPTOTIC STABILITY; EXISTENCE; CRITERIA; DIFFERENTIATION; EQUATIONS;
D O I：
10.1016/j.isatra.2015.11.008
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, a class of recurrent neural networks with multi-proportional delays is studied. The non-linear transformation transforms a class of recurrent neural networks with multi-proportional delays into a class of recurrent neural networks with constant delays and time-varying coefficients. By constructing Lyapunov functional and establishing the delay differential inequality, several delay-dependent and delay-independent sufficient conditions are derived to ensure global exponential periodicity and stability of the system. And several examples and their simulations are given to illustrate the effectiveness of obtained results. (C) 2015 ISA. Published by Elsevier Ltd. All rights reserved.
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页码：89 / 95
页数：7
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