Boosting(机器学习)
混乱的
吸引子
分叉
计算机科学
记忆电阻器
多稳态
复杂动力学
生物神经元模型
偏移量(计算机科学)
控制理论(社会学)
爆裂
振幅
物理
拓扑(电路)
统计物理学
人工神经网络
人工智能
数学
工程类
电气工程
非线性系统
控制(管理)
量子力学
神经科学
数学分析
生物
程序设计语言
作者
Yongxin Li,Chunbiao Li,Tengfei Lei,Yong Yang,Guanrong Chen
出处
期刊:IEEE Transactions on Industrial Electronics
[Institute of Electrical and Electronics Engineers]
日期:2023-11-01
卷期号:: 1-11
被引量:14
标识
DOI:10.1109/tie.2023.3325558
摘要
A discrete memristor is introduced into the Rulkov neuron to mimic biological neuronal synapse and modify firing dynamics. In the memristive Rulkov neuron, chaotic firing with local amplitude control is obtained, where the range of chaotic bursting can be modified by two independent controllers. These two independent bifurcation parameters provide direct amplitude/frequency control. Furthermore, offset boosting-entangled complex dynamics are captured, where the initial condition of the membrane potential can visit any of the self-reproducing attractors and even modify the complex firing, indicating the coexistence of homogeneous and heterogeneous multistabilities. Consequently, a CH32-based circuit is developed to verify various firing activities. The pseudo-random number generator results are explored based on the National Institute of Standards and Technology showing its higher performance in secure optical communication, which is further proved in the seven-core 2-km communication setup.
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