A Multi-Stable Memristor and its Application in a Neural Network

被引：185
|
作者：
Lin, Hairong [1 ]
Wang, Chunhua [1 ]
Hong, Qinghui [1 ]
Sun, Yichuang [2 ]
机构：
[1] Hunan Univ, Coll Comp Sci & Elect Engn, Changsha 410082, Hunan, Peoples R China
[2] Univ Hertfordshire, Sch Engn & Comp Sci, Hatfield AL10 9AB, Herts, England
来源：
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS | 2020年 / 67卷 / 12期
基金：
中国国家自然科学基金;
关键词：
Memristors; Mathematical model; Integrated circuit modeling; Hysteresis; Neural networks; Numerical simulation; Multistability; memristor; circuit theory; neural networks; nonlinear circuits;
D O I：
10.1109/TCSII.2020.3000492
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：
Nowadays, there is a lot of study on memristor-based systems with multistability. However, there is no study on memristor with multistability. This brief constructs a mathematical memristor model with multistability. The origin of the multi-stable dynamics is revealed using standard nonlinear theory as well as circuit and system theory. Moreover, the multi-stable memristor is applied to simulate a synaptic connection in a Hopfield neural network. The memristive neural network successfully generates infinitely many coexisting chaotic attractors unobserved in previous Hopfield-type neural networks. The results are also confirmed in analog circuits based on commercially available electronic elements.
引用
收藏
页码：3472 / 3476
页数：5
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