Dynamical Analysis and Image Encryption Application of Discrete Chaotic Neural Networks with Memristive Electromagnetic Radiation

Memristor can enhance the nonlinearity of neural networks, promoting complex and diverse dynamic behaviors. This paper simulates external electromagnetic radiation in neural networks using a discrete memristor, constructing a new memristive neural network. Numerical analysis shows that, as parameters vary, the proposed network can undergo complex bifurcation processes, generating periodic, chaotic, and hyperchaotic attractors. Meanwhile, the network exhibits parameter-relied signal modulation behaviors, such as amplitude control and offset-boosting. As 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, employing 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 information.