Novel results on bifurcation for a fractional-order complex-valued neural network with leakage delay

被引：30
|
作者：
Yuan, Jun [1 ]
Zhao, Lingzhi [1 ]
Huang, Chengdai [2 ]
Xiao, Min [3 ]
机构：
[1] Nanjing Xiaozhuang Univ, Sch Informat Engn, Nanjing 211171, Jiangsu, Peoples R China
[2] Xinyang Normal Univ, Sch Math & Stat, Xinyang 464000, Peoples R China
[3] Nanjing Univ Posts & Telecommun, Coll Automat, Nanjing 210003, Jiangsu, Peoples R China
来源：
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS | 2019年 / 514卷
基金：
中国国家自然科学基金;
关键词：
Leakage delays; Stability; Hopf bifurcation; Fractional order; Complex-valued neural networks; STABILITY ANALYSIS; HOPF BIFURCATIONS; TIME DELAYS;
D O I：
10.1016/j.physa.2018.09.138
中图分类号：
O4 [物理学];
学科分类号：
0702 ;
摘要：
This paper primarily investigates the impact of leakage delay on bifurcation for a fractional order complex-valued neural network. By means of time delay as a bifurcation parameter, the bifurcation conditions are precisely determined of the proposed novel system. It is pointed out that the stability performance of the addressed fractional neural network is extremely undermined when leakage delay appears by utilizing comparative numerical analysis, they cannot be discarded. Our obtained results enormously generalizes and enhances the existing ones in literatures. Numerical simulations are presented to verify the validity of the obtained results. (C) 2018 Elsevier B.V. All rights reserved.
引用
收藏
页码：868 / 883
页数：16
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