Analysis of global O(t-α) stability and global asymptotical periodicity for a class of fractional-order complex-valued neural networks with time varying delays

被引：63

作者：

Rakkiyappan, R. ^{[1
]}

Sivaranjani, R. ^{[1
]}

Velmurugan, G. ^{[1
]}

Cao, Jinde ^{[2
,3
]}

机构：

[1] Bharathiar Univ, Dept Math, Coimbatore 641046, Tamil Nadu, India

[2] Southeast Univ, Dept Math, Nanjing 210096, Jiangsu, Peoples R China

[3] Southeast Univ, Res Ctr Complex Syst & Network Sci, Nanjing 210096, Jiangsu, Peoples R China

来源：

NEURAL NETWORKS | 2016年 / 77卷

基金：

中国国家自然科学基金;

关键词：

Complex-valued neural networks; Fractional-order neural networks; Globally asymptotic periodicity; S-asymptotically periodic solution; Time-varying delays; ASSOCIATIVE MEMORY NETWORKS; EXPONENTIAL STABILITY; DISTRIBUTED DELAYS; DIFFERENTIAL-EQUATIONS; ROBUST STABILITY; MIXED DELAYS; COEFFICIENTS; EXISTENCE; DYNAMICS;

D O I：

10.1016/j.neunet.2016.01.007

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 problem of the global O(t(-alpha)) stability and global asymptotic periodicity for a class of fractional-order complex-valued neural networks (FCVNNs) with time varying delays is investigated. By constructing suitable Lyapunov functionals and a Leibniz rule for fractional differentiation, some new sufficient conditions are established to ensure that the addressed FCVNNs are globally O(t(-alpha)) stable. Moreover, some sufficient conditions for the global asymptotic periodicity of the addressed FCVNNs with time varying delays are derived, showing that all solutions converge to the same periodic function. Finally, numerical examples are given to demonstrate the effectiveness and usefulness of our theoretical results. (C) 2016 Elsevier Ltd. All rights reserved.

引用

页码：51 / 69

页数：19

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