A MBPAF-memristive Hopfield neural network and its application in image encryption

The complexity of neural dynamics heavily depends on the nonlinear activation functions, and a mixed-bipower activation function (MBPAF) with adjustable parameters is designed for the memristive Hopfield neural network (MHNN) to generate complex hyper-chaotic behaviors. Based on the designed MBPAF, a novel MBPAF-memristive Hopfield neural network (MBPAF-MHNN) model is proposed. The complex dynamics of the proposed MBPAF-MHNN model are validated through numerical analyses and further verified via FPGA implementation. Finally, a robust image encryption scheme is designed based on the MBPAF-MHNN model, featuring a plaintext-related "Diffusion-Permutation-Diffusion" architecture with DNA-based operations.