A new fractional-order chaos system of Hopfield neural network and its application in image encryption. (April 2022)

• Record Type: Journal Article
• Authors: Xu, Shaochuan; Wang, Xingyuan; Ye, Xiaolin
• Is Part Of: Chaos, solitons and fractals. Volume 157(2022)
• Publication Date: 2022-04
• Subjects: Chaos -- Hopfield neural network -- Adomain decomposition method -- Lyapunov exponents -- Image encryption; Chaotic behavior in systems -- Periodicals; Solitons -- Periodicals; Fractals -- Periodicals; Chaotic behavior in systems; Fractals; Solitons; Periodicals; 003.7
• DOI: 10.1016/j.chaos.2022.111889
• Languages: English
• ISSNs: 0960-0779

Abstract

Highlights: A new model of fractional-order HNN system is proposed. The chaos performance of the proposed system is analyzed. The new encryption algorithm is designed based on the proposed fractional-order HNN system, and the NIST test is tested. The security of the algorithm such as NPCR, UACI are compared and analyzed. 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.