含忆阻电磁辐射的离散混沌神经网络动力学分析及其图像加密应用OA

Memristor can enhance the nonlinearity of neural networks,enabling them to exhibit com-plex and diverse dynamic behaviors.This paper simulates external electromagnetic radiation in neural net-works and constructs a new memristive neural network by using a discrete memristor.Numerical analysis results show that,as parameters vary,the proposed network can undergo complex bifurcation processes,leading to the formation of periodic,chaotic,and hyperchaotic attractors.Meanwhile,the network exhibits parameter-relied signal modulation behaviors,such as amplitude control and offset-boosting.As the initial values change,the network can generate different types of extreme multistability.Furthermore,based on the proposed memristive neural network,this paper designs a new encryption algorithm for medical images.The algorighm employs diagonal diffusion and triple-bit mutation methods to conceal image information.Relevant tests demonstrate that the algorithm can effectively protect the security of medical image informa-tion.