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Bifurcations in a fractional-order neural network with multiple leakage delays.

Authors :
Huang, Chengdai
Liu, Heng
Shi, Xiangyun
Chen, Xiaoping
Xiao, Min
Wang, Zhengxin
Cao, Jinde
Source :
Neural Networks. Nov2020, Vol. 131, p115-126. 12p.
Publication Year :
2020

Abstract

This paper expatiates the stability and bifurcation for a fractional-order neural network (FONN) with double leakage delays. Firstly, the characteristic equation of the developed FONN is circumspectly researched by employing inequable delays as bifurcation parameters. Simultaneously the bifurcation criteria are correspondingly extrapolated. Then, unequal delays-spurred-bifurcation diagrams are primarily delineated to confirm the precision and correctness for the values of bifurcation points. Furthermore, it lavishly illustrates from the evidence that the stability performance of the proposed FONN can be demolished with the presence of leakage delays in accordance with comparative studies. Eventually, two numerical examples are exploited to underpin the feasibility of the developed theory. The results derived in this paper have perfected the retrievable outcomes on bifurcations of FONNs embodying unique leakage delay, which can nicely serve a benchmark deliberation and provide a comparatively credible guidance for the influence of multiple leakage delays on bifurcations of FONNs. [ABSTRACT FROM AUTHOR]

Subjects

Subjects :
*LEAKAGE
*HOPF bifurcations

Details

Language :
English
ISSN :
08936080
Volume :
131
Database :
Academic Search Index
Journal :
Neural Networks
Publication Type :
Academic Journal
Accession number :
146250250
Full Text :
https://doi.org/10.1016/j.neunet.2020.07.015