记忆电阻器
霍普夫分叉
分叉
人工神经网络
联轴节(管道)
数学
控制理论(社会学)
分叉理论的生物学应用
理论(学习稳定性)
鞍结分岔
Hopfield网络
拓扑(电路)
物理
计算机科学
非线性系统
量子力学
材料科学
人工智能
组合数学
机器学习
冶金
控制(管理)
作者
Tao Dong,Xiaomei Gong,Tingwen Huang
标识
DOI:10.1016/j.neunet.2022.02.009
摘要
This paper proposes a novel memristive synaptic Hopfield neural network (MHNN) with time delay by using a memristor synapse to simulate the electromagnetic induced current caused by the membrane potential difference between two adjacent neurons. First, some sufficient conditions of zero bifurcation and zero-Hopf bifurcation are obtained by choosing time delay and coupling strength of memristor as bifurcation parameters. Then, the third-order normal form of zero-Hopf bifurcation is obtained. By analyzing the obtained normal form, six dynamic regions are found on the plane with coupling strength of memristor and time delay as abscissa and ordinate. There are some interesting dynamics in these areas, i.e., the coupling strength of memristor can affect the number and dynamics of system equilibrium, time delay can contribute to both trivial equilibrium and non-trivial equilibrium losing stability and generating periodic solutions.
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