多稳态
吸引子
记忆电阻器
人工神经网络
李雅普诺夫指数
混乱的
Hopfield网络
相空间
拓扑(电路)
计算机科学
相图
偏移量(计算机科学)
统计物理学
分叉
控制理论(社会学)
物理
数学
人工智能
非线性系统
数学分析
控制(管理)
量子力学
组合数学
程序设计语言
作者
Hairong Lin,Chunhua Wang,Fei Yu,Qinghui Hong,Cong Xu,Yichuang Sun
标识
DOI:10.1109/tcad.2023.3287760
摘要
Memristors have recently demonstrated great promise in constructing memristive neural networks with complex dynamics. This article proposes a memristive Hopfield neural network with three memristive coupling synaptic weights. The complex dynamical behaviors of the triple-memristor Hopfield neural network (TM-HNN), which have never been observed in previous Hopfield-type neural networks, include space multistructure chaotic attractors and space initial-offset coexisting behaviors. Bifurcation diagrams, Lyapunov exponents, phase portraits, Poincaré maps, and basins of attraction are used to reveal and examine the specific dynamics. Theoretical analysis and numerical simulation show that the number of space multistructure attractors can be adjusted by changing the control parameters of the memristors, and the position of space coexisting attractors can be changed by switching the initial states of the memristors. Extreme multistability emerges as a result of the TM-HNN’s unique dynamical behaviors, making it more suitable for applications based on chaos. Moreover, a digital hardware platform is developed and the space multistructure attractors as well as the space coexisting attractors are experimentally demonstrated. Finally, we design a pseudorandom number generator to explore the potential application of the proposed TM-HNN.
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