忆阻Hopfield神经网络的初值位移调控动力学及其图像加密应用

Initial Offset Boosting Dynamics in A Memristive Hopfield Neural Network and Its Application in Image Encryption

利用改进的多稳态忆阻器模拟神经元耦合突触,提出一种忆阻Hopfield神经网络(HNN)模型。用分岔图、Lyapunov指数谱、相图、庞加莱截面等对其动力学行为进行理论分析与数值仿真。结果表明:该忆阻HNN不仅能够产生不同拓扑结构的混沌吸引子,而且能够产生高度依赖忆阻初值的位移调控动力学行为。最后,基于该忆阻HNN设计一种混沌图像加密系统,重点分析系统的直方图、相关性、信息裔以及密钥敏感性。结果表明:所设计的图像加密算法能够有效抵抗各种内外统计分析攻击,具有较高的安全性。

A memristive Hopfield neural network(HNN)model is proposed in which an improved multi-stable memristor is used to simulate coupled neuron synapses.Dynamical behavior of the model is analyzed and simulated with bifurcation diagram,Lyapunov exponential spectrum,phase plot and Poincare section.It shows that the memristive HNN generates chaotic attractors with different topologies and generates initial offset boosting highly dependent on initial value of the memristor.Finally,a chaotic image encryption scheme is designed based on the memristive HNN.The histogram,correlation,information entropy and key sensitivity are analyzed.It shows that the image encryption scheme resists effectively various internal and external statistical analysis attacks and has higher security.