Memristor Synapse-Driven Simplified Hopfield Neural Network: Hidden Dynamics, Attractor Control, and Circuit Implementation

被引:42
|
作者
Chen, Chengjie [1 ]
Min, Fuhong [1 ]
Cai, Jianming [2 ]
Bao, Han [2 ]
机构
[1] Nanjing Normal Univ, Sch Elect & Automat Engn, Sch Comp & Elect Informat, Sch Artificial Intelligence, Nanjing 210023, Peoples R China
[2] Changzhou Univ, Sch Microelect & Control Engn, Changzhou 213164, Peoples R China
基金
中国国家自然科学基金;
关键词
Memristor; chaos; attractor control; Hopfield neural network (HNN); hidden dynamics; circuit implementation;
D O I
10.1109/TCSI.2024.3349451
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Detection of hidden dynamics is of great value in model prediction and control engineering. To explore its effects and control methods in the memristive network model, this paper presents a memristor synapse-driven ReLU-type Hopfield neural network (MRHNN). The generalized Hamilton function is derived from Helmholtz's theorem and the equilibrium points of the model are analyzed. It is found via numerical computations that because of no existence of equilibrium, the MRHNN model always unfolds hidden dynamics, including hidden bifurcation, hidden mode transition, hidden transient chaos, and hidden multistability. In addition, amplitude and offset boosting control of hidden attractors are executed, illustrating the flexibility of the attractor regulation. Finally, based on digital hardware devices, circuit experiments are deployed and their measurements well agree with the numerical results, certifying the dynamical effects and lossless control of the memristive neural network and physical reliability of the electronic neuron.
引用
收藏
页码:2308 / 2319
页数:12
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