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

abstract

• 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.

authors

• Huang, Tingwen

published proceedings

• Neural Netw

author list (cited authors)

• Chen, L., Yin, H., Huang, T., Yuan, L., Zheng, S., & Yin, L.

citation count

• 112

complete list of authors

• Chen, Liping||Yin, Hao||Huang, Tingwen||Yuan, Liguo||Zheng, Song||Yin, Lisheng

publication date

• May 2020

publisher

• Elsevier  Publisher

published in

• Neural Networks  Journal

keywords

• Algorithms
• Computer Security
• Fractional-order Discrete Systems
• Humans
• Image Encryption
• Neural Networks
• Neural Networks, Computer
• Nonlinear Dynamics
• Pattern Recognition, Automated
• Synchronization

PubMed Central ID

• 32135353

Digital Object Identifier (DOI)

• 10.1016/j.neunet.2020.02.008

start page

• 174

end page

• 184

volume

• 125

URL

• http://dx.doi.org/10.1016/j.neunet.2020.02.008