局部有源忆阻耦合异质神经元的设计及在DNA编码图像加密的应用

王子成; 马永幸; 王延峰; 孙军伟

doi:10.11999/JEIT221493

局部有源忆阻耦合异质神经元的设计及在DNA编码图像加密的应用

doi: 10.11999/JEIT221493

郑州轻工业大学电气信息工程学院郑州 450002

基金项目: 国家自然科学基金(62276239, 62272424)，中原青年拔尖人才项目(ZYYCYU202012154)

详细信息

作者简介:
王子成：男，副教授，研究方向为非线性电路设计及应用

马永幸：男，硕士生，研究方向为非线性电路设计及应用

王延峰：男，教授，研究方向为非线性电路设计及应用

孙军伟：男，副教授，研究方向为类脑智能设计与电路实现、非线性电路设计及应用

通讯作者:
孙军伟　junweisun@yeah.net

中图分类号: TN711.4
计量
- 文章访问数: 420
- HTML全文浏览量: 210
- PDF下载量: 69
- 被引次数: 0
出版历程
- 收稿日期: 2022-12-12
- 修回日期: 2023-04-12
- 网络出版日期: 2023-04-25
- 刊出日期: 2023-09-27

Design of Locally Active Memristor Coupled Heterogeneous Neurons and Its Application to DNA Encoded Image Encryption

School of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China

Funds: The National Natural Science Foundation of China (62276239, 62272424), Zhongyuan Talents Program (ZYYCYU202012154)

摘要

摘要: 不同的神经元之间具有异质性，动力学特性也有很大不同，因此异质神经元之间的耦合是一个有价值的研究方向。该文利用Fitzhugh-Nagumo (FN)神经元和Hindmarsh-Rose (HR)神经元构造一个局部有源忆阻耦合异质神经元。对局部有源忆阻耦合异质神经元的分岔图、谱熵和3参数李雅普诺夫指数图等进行分析，该异质神经元存在多周期窗等丰富的动力学特性。为增强图像传输的安全性设计一种基于局部有源忆阻耦合异质神经元的DNA编码图像加密算法。对加密图像的噪声和裁剪分析表明该图像加密算法具有较强的鲁棒性。
- 忆阻 /
- 异质神经元 /
- 3参数李雅普诺夫指数 /
- DNA编码 /
- 图像加密
Abstract: There is heterogeneity between different neurons, characteristics of dynamics are also quite different, so the coupling between heterogeneous neurons is a valuable research direction. In this paper, a locally active memristor coupled heterogeneity neuron is constructed using Fitzhugh-Nagumo (FN) neuron and Hindmarsh-Rose (HR) neuron. The bifurcation diagrams, spectral entropy and three parameter Lyapunov exponential diagrams of the heterogeneous neuron system are analyzed, and the heterogeneous neurons are multiple period windows and rich dynamic characteristics. In order to enhance the security of image transmission, a DNA encoded image encryption algorithm based on locally active memristor coupled heterogeneous neurons is designed. The noise and clipping of the encrypted image are analyzed, and the results show that the algorithm has strong robustness.
- Memristor /
- Heterogeneous neurons /
- Three parameter Lyapunov exponent /
- DNA encoded /
- Image encryption

HTML全文

图 1 磁滞回线

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图 2 局部有源性

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图 3 系统随参数b变化时的动力学特性

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图 4 复杂性分析，b=3

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图 5 3参数的最大李雅普诺夫指数图

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图 6 主电路

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图 7 仿真结果

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图 8 加密流程图

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图 9 原始图像和加密图像

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图 10 直方图分析

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图 11 相关性分析

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图 12 椒盐和胡椒噪声攻击后的解密结果

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图 13 裁剪攻击

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表 1 不同算法下信息熵的比较

	原始Lena图像	文献[18]	文献[20]	文献[21]	本文算法
信息熵	7.2283	7.9971	7.9485	7.9951	7.9972

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表 2 不同算法下相关系数的比较

	文献[15]	文献[18]	文献[20]	文献[22]	本文算法
水平方向	0.0019	0.0083	0.0029	–0.1224	0.0004
垂直方向	–0.0030	–0.0021	–0.0342	0.0046	–0.0013
对角线方向	0.0018	–0.0025	–0.0021	–0.0136	–0.0021

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参考文献(22)

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