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

作　　者：李晓霞 何倩倩 余天意 才壮 徐桂芝 Xiaoxia Li;Qianqian He;Tianyi Yu;Zhuang Cai;Guizhi Xu(Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability of Hebei Province,Hebei University of Technology,Tianjin 300130,China;State Key Laboratory of Reliability and Intelligence of Electrical Equipment,Hebei University of Technology,Tianjin 300130,China;School of Life Science & Health Engineering,Hebei University of Technology,Tianjin 300130,China)

机构地区：[1]Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability of Hebei Province,Hebei University of Technology,Tianjin 300130,China [2]State Key Laboratory of Reliability and Intelligence of Electrical Equipment,Hebei University of Technology,Tianjin 300130,China [3]School of Life Science & Health Engineering,Hebei University of Technology,Tianjin 300130,China

出　　处：《Chinese Physics B》2024年第3期302-315,共14页中国物理B（英文版）

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

摘　　要：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.

关 键 词：hyperbolic-type memristor Hopfield neural network(HNN) asymmetric attractors image encryption 

分 类 号：TN60[电子电信—电路与系统] TP183[自动化与计算机技术—控制理论与控制工程]