Chaos in fractional-order discrete neural networks with application to image encryption

被引：197

作者：

Chen, Liping ^{[1
]}

Yin, Hao ^{[1
]}

Huang, Tingwen ^{[2
]}

Yuan, Liguo ^{[3
]}

Zheng, Song ^{[4
]}

Yin, Lisheng ^{[1
]}

机构：

[1] Hefei Univ Technol, Sch Elect Engn & Automat, Hefei 230009, Anhui, Peoples R China

[2] Texas A&M Univ Qatar, POB 23874, Doha, Qatar

[3] South China Agr Univ, Coll Math & Informat, Guangzhou 510642, Guangdong, Peoples R China

[4] Zhejiang Univ Finance & Econ, Sch Data Sci, Dept Math, Hangzhou 310018, Zhejiang, Peoples R China

来源：

NEURAL NETWORKS | 2020年 / 125卷 / 125期

关键词：

Fractional-order discrete systems; Neural networks; Synchronization; Image encryption; MITTAG-LEFFLER STABILITY; DECISION-MAKING METHOD; PROJECTIVE SYNCHRONIZATION; ALGORITHM; TIME;

D O I：

10.1016/j.neunet.2020.02.008

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, a three-dimensional fractional-order (FO) discrete Hopfield neural network (FODHNN) in the left Caputo discrete delta's sense is proposed, the dynamic behavior and synchronization of FODHNN are studied, and the system is applied to image encryption. First, FODHNN is shown to exhibit rich nonlinear dynamics behaviors. Phase portraits, bifurcation diagrams and Lyapunov exponents are carried out to verify chaotic dynamics in this system. Moreover, by using stability theorem of FO discrete linear systems, a suitable control scheme is designed to achieve synchronization of the FODHNN. Finally, image encryption system based on the chaotic FODHNN is presented. Some security analysis and tests are given to show the effective of the encryption system. (c) 2020 Elsevier Ltd. All rights reserved.

引用

页码：174 / 184

页数：11

共 40 条

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