Dynamical bifurcations in a delayed fractional‐order neural network involving neutral terms

The stability and bifurcations of a fractional‐order neural network with a neutral delay are nicely contemplated with the help of the Cramer's rule. The three‐neuron neutral‐type fractional‐order neural network (NTFONN) is firstly constructed. Secondly, the Laplace transform of the Caputo fractional‐order derivatives is used. Afterward, using the analytical method of characteristic equations and Cramer's rule, the existence of Hopf bifurcations is obtained. Moreover, it indicates that the neutral delay plays an enormously significant role in remaining network stabilization and controlling the occurrence of Hopf bifurcations in NTFONN. It further detects that the devised NTFONN has outstanding stability performance in comparison with the corresponding integer‐order one. Finally, numerical simulations are developed to confirm the feasibility and validity of the obtained results.

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Publication Details

Journal
Mathematical Methods in the Applied Sciences
Published
2024-09-04
DOI
https://doi.org/10.1002/mma.10434
Citations
1
Primary Topic
Neural Networks and Applications
Type
article
Field-Weighted Citation Impact
0.35

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article

Dynamical bifurcations in a delayed fractional‐order neural network involving neutral terms

Mahmoud Abdel‐Aty, Jinde Cao, Chengdai Huang, Shuang Liu et al.
1 citations
Mathematical Methods in the Applied Sciences
Neural Networks and Applications
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article

Dynamical bifurcations in a delayed fractional‐order neural network involving neutral terms

Mahmoud Abdel‐Aty, Jinde Cao, Chengdai Huang, Shuang Liu, Heng Liu, Lei Fu
article en
1 citations

Abstract

The stability and bifurcations of a fractional‐order neural network with a neutral delay are nicely contemplated with the help of the Cramer's rule. The three‐neuron neutral‐type fractional‐order neural network (NTFONN) is firstly constructed. Secondly, the Laplace transform of the Caputo fractional‐order derivatives is used. Afterward, using the analytical method of characteristic equations and Cramer's rule, the existence of Hopf bifurcations is obtained. Moreover, it indicates that the neutral delay plays an enormously significant role in remaining network stabilization and controlling the occurrence of Hopf bifurcations in NTFONN. It further detects that the devised NTFONN has outstanding stability performance in comparison with the corresponding integer‐order one. Finally, numerical simulations are developed to confirm the feasibility and validity of the obtained results.

Mathematical Methods in the Applied SciencesVol. 48(2)
Ahlia University (BH), Xinyang Normal University (CN), Minzu University of China (CN), Jadara University (JO), Southeast University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 34%
Neural Networks and Applications
0.35
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