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Delay-dependent parameters bifurcation in a fractional neural network via geometric methods

Author

Listed:
  • Li, Shuai
  • Cao, Jinde
  • Liu, Heng
  • Huang, Chengdai

Abstract

Delayed fractional neural network models are addressed to describe the effects of signal losses due to delay, the memory of neurons and self-feedback delay on the dynamics. The stable intervals of communication delay are first determined when the self-feedback delay is absent. The network with signal losses can exhibit stability switches. Then, the criteria for the generation of Hopf bifurcation for self-feedback delay are established by treating communication delay as a constant and it is shown that self-feedback delay can induce changes in stability for the equilibrium as it crosses the first Hopf bifurcation point. The stable regions in the delay plane are also plotted by the geometric method. Finally, two illustrative examples are provided to corroborate the efficiency of the procured results. The obtained results illustrate that the stable regions for delays become larger as fractional order decreases, which indicates that the memory of neurons can dampen the oscillatory behaviors.

Suggested Citation

  • Li, Shuai & Cao, Jinde & Liu, Heng & Huang, Chengdai, 2024. "Delay-dependent parameters bifurcation in a fractional neural network via geometric methods," Applied Mathematics and Computation, Elsevier, vol. 478(C).
  • Handle: RePEc:eee:apmaco:v:478:y:2024:i:c:s009630032400273x
    DOI: 10.1016/j.amc.2024.128812
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    References listed on IDEAS

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    1. Huang, Chengdai & Cao, Jinde & Xiao, Min & Alsaedi, Ahmed & Hayat, Tasawar, 2017. "Bifurcations in a delayed fractional complex-valued neural network," Applied Mathematics and Computation, Elsevier, vol. 292(C), pages 210-227.
    2. Li, Shuai & Huang, Chengdai & Song, Xinyu, 2023. "Detection of Hopf bifurcations induced by pregnancy and maturation delays in a spatial predator–prey model via crossing curves method," Chaos, Solitons & Fractals, Elsevier, vol. 175(P1).
    3. Chen, Jing & Xiao, Min & Wu, Xiaoqun & Wang, Zhengxin & Cao, Jinde, 2022. "Spatiotemporal dynamics on a class of (n+1)-dimensional reaction–diffusion neural networks with discrete delays and a conical structure," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).
    4. Ozturk Mizrak, Ozlem & Mizrak, Cihan & Kashkynbayev, Ardak & Kuang, Yang, 2020. "Can fractional differentiation improve stability results and data fitting ability of a prostate cancer model under intermittent androgen suppression therapy?," Chaos, Solitons & Fractals, Elsevier, vol. 131(C).
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