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Comparative exploration on bifurcation behavior for integer-order and fractional-order delayed BAM neural networks

Authors :
Changjin Xu
Dan Mu
Zixin Liu
Yicheng Pang
Maoxin Liao
Peiluan Li
Lingyun Yao
Qiwen Qin
Source :
Nonlinear Analysis, Vol 27 (2022)
Publication Year :
2022
Publisher :
Vilnius University Press, 2022.

Abstract

In the present study, we deal with the stability and the onset of Hopf bifurcation of two type delayed BAM neural networks (integer-order case and fractional-order case). By virtue of the characteristic equation of the integer-order delayed BAM neural networks and regarding time delay as critical parameter, a novel delay-independent condition ensuring the stability and the onset of Hopf bifurcation for the involved integer-order delayed BAM neural networks is built. Taking advantage of Laplace transform, stability theory and Hopf bifurcation knowledge of fractional-order differential equations, a novel delay-independent criterion to maintain the stability and the appearance of Hopf bifurcation for the addressed fractional-order BAM neural networks is established. The investigation indicates the important role of time delay in controlling the stability and Hopf bifurcation of the both type delayed BAM neural networks. By adjusting the value of time delay, we can effectively amplify the stability region and postpone the time of onset of Hopf bifurcation for the fractional-order BAM neural networks. Matlab simulation results are clearly presented to sustain the correctness of analytical results. The derived fruits of this study provide an important theoretical basis in regulating networks.

Details

Language :
English
ISSN :
13925113 and 23358963
Volume :
27
Database :
Directory of Open Access Journals
Journal :
Nonlinear Analysis
Publication Type :
Academic Journal
Accession number :
edsdoj.34c0f2e7938349d0bd609fbd10e897cf
Document Type :
article
Full Text :
https://doi.org/10.15388/namc.2022.27.28491