Generating n-Scroll Chaotic Attractors From a Memristor-Based Magnetized Hopfield Neural Network

被引：100
|
作者：
Lin, Hairong [1 ]
Wang, Chunhua [1 ]
Sun, Yichuang [2 ]
Wang, Ting [3 ]
机构：
[1] Hunan Univ, Coll Comp Sci & Elect Engn, Changsha 410082, Peoples R China
[2] Univ Hertfordshire, Sch Engn & Comp Sci, Hatfield AL10 9AB, Herts, England
[3] Hunan Univ Finance & Econ, Changsha 410205, Peoples R China
来源：
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS | 2023年 / 70卷 / 01期
基金：
中国博士后科学基金; 中国国家自然科学基金;
关键词：
Neurons; Memristors; Magnetic resonance imaging; Magnetic circuits; Magnetic flux; Membrane potentials; Hopfield neural networks; Memristor; hopfield neural network; multi-scroll attractor; initial offset boosting; circuit implementation; DESIGN; SYSTEMS;
D O I：
10.1109/TCSII.2022.3212394
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：
This brief presents a novel method to generate n-scroll chaotic attractors. First, a magnetized Hopfield neural network (HNN) with three neurons is modeled by introducing an improved multi-piecewise memristor to describe the effect of electromagnetic induction. Theoretical analysis and numerical simulation show that the memristor-based magnetized HNN can generate multi-scroll chaotic attractors with arbitrary number of scrolls. The number of scrolls can be easily changed by adjusting the memristor control parameters. Besides, complex initial offset boosting behavior is revealed from the magnetized HNN. Finally, a magnetized HNN circuit is designed and various typical attractors are verified.
引用
收藏
页码：311 / 315
页数：5
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