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

页码：3472 / 3476

页数：5

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