Coexistence behavior of asymmetric attractors in hyperbolic-type memristive Hopfield neural network and its application in image encryption

doi:10.1088/1674-1056/acf281

Abstract The neuron model has been widely employed in neural-morphic computing systems and chaotic circuits. This study aims to develop a novel circuit simulation of a three-neuron Hopfield neural network (HNN) with coupled hyperbolic memristors through the modification of a single coupling connection weight. The bistable mode of the hyperbolic memristive HNN (mHNN), characterized by the coexistence of asymmetric chaos and periodic attractors, is effectively demonstrated through the utilization of conventional nonlinear analysis techniques. These techniques include bifurcation diagrams, two-parameter maximum Lyapunov exponent plots, local attractor basins, and phase trajectory diagrams. Moreover, an encryption technique for color images is devised by leveraging the mHNN model and asymmetric structural attractors. This method demonstrates significant benefits in correlation, information entropy, and resistance to differential attacks, providing strong evidence for its effectiveness in encryption. Additionally, an improved modular circuit design method is employed to create the analog equivalent circuit of the memristive HNN. The correctness of the circuit design is confirmed through Multisim simulations, which align with numerical simulations conducted in Matlab.

Keywords: hyperbolic-type memristor Hopfield neural network (HNN) asymmetric attractors image encryption

Received: 20 June 2023
Revised: 16 August 2023
Accepted manuscript online: 22 August 2023

PACS:

05.45.-a

(Nonlinear dynamics and chaos)

Fund: Project supported by the National Nature Science Foundation of China (Grant Nos. 51737003 and 51977060) and the Natural Science Foundation of Hebei Province (Grant No. E2011202051).

Corresponding Authors: Xiaoxia Li
E-mail: lixiaoxia@hebut.edu.cn

Cite this article:

Xiaoxia Li(李晓霞), Qianqian He(何倩倩), Tianyi Yu(余天意),Zhuang Cai(才壮), and Guizhi Xu(徐桂芝) Coexistence behavior of asymmetric attractors in hyperbolic-type memristive Hopfield neural network and its application in image encryption 2024 Chin. Phys. B 33 030505

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Coexistence behavior of asymmetric attractors in hyperbolic-type memristive Hopfield neural network and its application in image encryption

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