Global stability criteria for nonlinear differential systems with infinite delay and applications to BAM neural networks

Oliveira, José J.

doi:10.1016/j.chaos.2022.112676

Cited by 12 publications

(1 citation statement)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, Li and Shen [6] dealt with the near-automorphic solution to Clifford-valued delayed fuzzy cellular neural networks; Xu et al [7] investigated the pseudo-near-periodic oscillation to quaternion-valued fuzzy delayed neural network models; Cui et al [8] carried out a detailed analysis on fixed-time synchronization in a kind of Markovian jump fuzzy stochastic delayed neural network models; and Huang et al [9] explored the anti-periodic oscillation to delayed neural network models. For more related studies, interested readers can refer to [10][11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Bifurcation Phenomenon and Control Technique in Fractional BAM Neural Network Models Concerning Delays

et al. 2022

Fractal Fract

View full text Add to dashboard Cite

In this current study, we formulate a kind of new fractional BAM neural network model concerning five neurons and time delays. First, we explore the existence and uniqueness of the solution of the formulated fractional delay BAM neural network models via the Lipschitz condition. Second, we study the boundedness of the solution to the formulated fractional delayed BAM neural network models using a proper function. Third, we set up a novel sufficient criterion on the onset of the Hopf bifurcation stability of the formulated fractional BAM neural network models by virtue of the stability criterion and bifurcation principle of fractional delayed dynamical systems. Fourth, a delayed feedback controller is applied to command the time of occurrence of the bifurcation and stability domain of the formulated fractional delayed BAM neural network models. Lastly, software simulation figures are provided to verify the key outcomes. The theoretical outcomes obtained through this exploration can play a vital role in controlling and devising networks.

show abstract

Section: Introductionmentioning

confidence: 99%

Bifurcation Phenomenon and Control Technique in Fractional BAM Neural Network Models Concerning Delays

et al. 2022

Fractal Fract

View full text Add to dashboard Cite

show abstract

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

Huang,

Fu,

Liu

et al. 2024

Math Methods in App Sciences

View full text Add to dashboard Cite

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.

show abstract

Anti-synchronization of fractional-order complex-valued neural networks with a leakage delay and time-varying delays

Liu

Wang

2023

Chaos, Solitons & Fractals

View full text Add to dashboard Cite

Global stability criteria for nonlinear differential systems with infinite delay and applications to BAM neural networks

Cited by 12 publications

References 42 publications

Bifurcation Phenomenon and Control Technique in Fractional BAM Neural Network Models Concerning Delays

Bifurcation Phenomenon and Control Technique in Fractional BAM Neural Network Models Concerning Delays

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

Anti-synchronization of fractional-order complex-valued neural networks with a leakage delay and time-varying delays

Contact Info

Product

Resources

About