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Enhanced bifurcation results for a delayed fractional neural network with heterogeneous orders

Author

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  • Huang, Chengdai
  • Tang, Jingyong
  • Niu, Yantao
  • Cao, Jinde

Abstract

This paper addresses the stability and bifurcation of a delayed fractional neural network(FNN) with different orders. Firstly, system parameter acts as a bifurcation parameter, and bifurcation criterion are educed for such system. It discovers that the stability performance of the proposed system can be exalted by selecting modest system parameter. Secondly, the bifurcation diagrams are fully illustrated for checking the exactness of the procured bifurcation results. Thirdly, in terms of discreet calculation, it perceives that the onset of bifurcation can be advanced by single order or time delay if as they decrease. Finally, two numerical examples are exploited to validate the efficiency of the theoretical results.

Suggested Citation

  • Huang, Chengdai & Tang, Jingyong & Niu, Yantao & Cao, Jinde, 2019. "Enhanced bifurcation results for a delayed fractional neural network with heterogeneous orders," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 526(C).
    • Handle: RePEc:eee:phsmap:v:526:y:2019:i:c:s0378437119306247
    DOI: 10.1016/j.physa.2019.04.250
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    Cited by:

    1. Xu, Changjin & Liu, Zixin & Liao, Maoxin & Li, Peiluan & Xiao, Qimei & Yuan, Shuai, 2021. "Fractional-order bidirectional associate memory (BAM) neural networks with multiple delays: The case of Hopf bifurcation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 182(C), pages 471-494.
    2. Huang, Chengdai & Liu, Heng & Chen, Xiaoping & Cao, Jinde & Alsaedi, Ahmed, 2020. "Extended feedback and simulation strategies for a delayed fractional-order control system," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).

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