Dynamics analysis and image encryption application of Hopfield neural network with a novel multistable and highly tunable memristor

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成果类型：

期刊论文

作者：

Yao, Wei;Liu, Jiapei;Sun, Yichuang;Zhang, Jin;Yu, Fei;...

通讯作者：

Yao, W

作者机构：

[Zhang, Jin; Yao, Wei; Yu, Fei; Liu, Jiapei] Changsha Univ Sci & Technol, Sch Comp & Commun Engn, Changsha 410114, Peoples R China.

[Yao, Wei] Hunan Normal Univ, Sch Math & Stat, Key Lab Comp & Stochast Math, Minist Educ, Changsha 410081, Hunan, Peoples R China.

[Yao, Wei] Zhejiang Univ, State Key Lab Ind Control Technol, Hangzhou, Peoples R China.

[Sun, Yichuang] Univ Hertfordshire, Sch Engn & Comp Sci, Hatfield AL10 9AB, England.

[Cui, Li] Hunan Univ Sci & Technol, Sch Informat & Elect Engn, Xiangtan 411201, Peoples R China.

通讯机构：

[Yao, W ] C

Changsha Univ Sci & Technol, Sch Comp & Commun Engn, Changsha 410114, Peoples R China.

Hunan Normal Univ, Sch Math & Stat, Key Lab Comp & Stochast Math, Minist Educ, Changsha 410081, Hunan, Peoples R China.

Zhejiang Univ, State Key Lab Ind Control Technol, Hangzhou, Peoples R China.

语种：

英文

关键词：

Chaotic dynamics;Asymmetric memristive Hopfield neural network;Highly tunable memristor;Lyapunov exponents and bifurcation;Image encryption

期刊：

Nonlinear Dynamics

ISSN：

0924-090X

年：

2024

卷：

112

期：

页码：

693-708

DOI：

10.1007/s11071-023-09041-1

基金类别：

Scientific Research Foundation of Hunan Provincial Education Department [62201204]; National Natural Science Foundation of China [2022JJ40514, 2022JJ30624]; Hunan Provincial Natural Science Foundation of China [21B0345, 21C0200]; Scientific Research Foundation of Hunan Provincial Education Department, China [kfj220603]; Open Fund of Engineering Laboratory of Spatial Information Technology of Highway Geological Disaster Early Warning in Hunan Province, Changsha University of Science and Technology, China [ICT2023B38]; Open Research Project of the State Key Laboratory of Industrial Control Technology, Zhejiang University, China

机构署名：

本校为第一且通讯机构

院系归属：

计算机与通信工程学院

摘要：

Building artificial neural network models and studying their dynamic behaviors is extremely important from both a theoretical and practical standpoint due to the rapid advancement of artificial intelligence . In addition to its engineering applications, this article concentrates primarily on the memristor model and chaotic dynamics of the asymmetric memristive neural network. First, we develop a novel memristor model, which is multistable and highly tunable. Using this memristor model to build an asymmetric memristive Hopfield neural network (AMHNN), the chaotic dynamics of the proposed AMHNN ...

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Dynamics analysis and image encryption application of Hopfield neural network with a novel multistable and highly tunable memristor

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