连续时延神经网络的Hopf分岔现象研究

Study of Bifurcation Phenomenon for Neural Network with Continuously Time Delay

  • 摘要: 讨论了带连续时延神经网络的Hopf分岔现象。对于强核和弱核的情况,利用平均时延作为分岔参数,证明了模型经历了Hopf分岔过程。在带弱核的神经网络模型中,得到了分岔周期解稳定性准则。给出了一些数值例子,通过计算机仿真验证了所得结论的正确性。

     

    Abstract: In this paper, we study the Hopf bifurcation phenomena of a neural network with a continuous time delay. Using the average time delay as bifurcation parameter, we have proved that the model undergoes a sequence of Hopf bifurcations in both the strong kernel and weak kernel cases. Stability criteria for the bifurcating periodic solutions are derived in the neural network with weak kernel. Some numerical examples and the computer simulation results are also presented to justify the theoretical results.

     

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