A new fractional-order chaos system of Hopfield neural network and its application in image encryption

Shaochuan Xu, Xingyuan Wang and Xiaolin Ye

Chaos, Solitons & Fractals, 2022, vol. 157, issue C

Abstract: In this work, we propose a new fractional-order chaotic system based on the model of 4-neurons-based Hopfield Neural Network (HNN). By using Adomain decomposition method, the proposed fractional-order chaotic system is solved. With the orders changing, the proposed fractional-order system shows rich dynamical characteristics. Then, based on the pseudo-random numbers (PRNs) generated by the proposed system, a new construction method of multiple hash index chain is designed. And a new image encryption algorithm is designed according to the multiple hash index chain. The safety test results show that the design encryption algorithm has higher security performance. Finally, the 4-neurons-based HNN fractional-order system is implemented by Multisim circuit simulation. The experimental results show the feasibility of the theoretical analysis.

Keywords: Chaos; Hopfield neural network; Adomain decomposition method; Lyapunov exponents; Image encryption (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (11)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S096007792200100X
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:157:y:2022:i:c:s096007792200100x

DOI: 10.1016/j.chaos.2022.111889

Access Statistics for this article

Chaos, Solitons & Fractals is currently edited by Stefano Boccaletti and Stelios Bekiros

More articles in Chaos, Solitons & Fractals from Elsevier
Bibliographic data for series maintained by Thayer, Thomas R. ().