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Local and global Hopf bifurcation analysis on simplified bidirectional associative memory neural networks with multiple delays

机译:具有多个时滞的简化双向联想记忆神经网络的局部和全局Hopf分叉分析

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摘要

In this paper, a class of simplified bidirectional associative memory (BAM) neural networks with multiple delays are considered. By analyzing the associated characteristic transcendental equation, their linear stability is investigated and Hopf bifurcation is demonstrated. By applying Nyquist criterion, the length of delay which preserves the stability of the zero equilibrium is estimated. Some explicit results are derived for stability and direction of the bifurcating periodic orbit by using the normal form theory and center manifold arguments. Global existence of periodic orbits is also established by using a global Hopf bifurcation theorem for functional differential equations (FDE) and a Bendixson’s criterion for high-dimensional ordinary differential equations (ODE) due to Li and Muldowney. Finally, numerical simulations supporting the theoretical analysis are carried out.
机译:本文考虑了一类具有多个时延的简化双向联想记忆(BAM)神经网络。通过分析相关的特征超越方程,研究了它们的线性稳定性,并证明了霍普夫分支。通过应用奈奎斯特准则,估计了保持零平衡稳定性的延迟长度。利用范式理论和中心流形参数,得出了分叉周期轨道的稳定性和方向的一些明确结果。通过使用针对函数微分方程(FDE)的全局Hopf分支定理和针对Li和Muldowney的高维常微分方程(ODE)的Bendixson准则,还可以确定周期轨道的全局存在性。最后,进行了支持理论分析的数值模拟。

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