时滞神经网络模型的全局稳定性、分岔和混沌控制

Amitava Kundu, P. Das
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引用次数: 2

摘要

导出了n(≥3)个神经元的延迟人工神经网络模型全局渐近稳定的条件。对于时滞的分岔分析,我们考虑了具有三个神经元的模型,并对多个时滞进行了适当的变换,将其简化为一个单时滞系统。讨论了单时延的分岔分析。数值模拟验证了分析结果。通过数值模拟,讨论了延迟和神经元增益参数对神经网络模型动力学特性的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Global Stability, Bifurcation, and Chaos Control in a Delayed Neural Network Model
Conditions for the global asymptotic stability of delayed artificial neural network model of n (≥3) neurons have been derived. For bifurcation analysis with respect to delay we have considered the model with three neurons and used suitable transformation on multiple time delays to reduce it to a system with single delay. Bifurcation analysis is discussed with respect to single delay. Numerical simulations are presented to verify the analytical results. Using numerical simulation, the role of delay and neuronal gain parameter in changing the dynamics of the neural network model has been discussed.
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