A 6D Fractional-Order Memristive Hopfield Neural Network and its Application in Image Encryption - Yu, Fei; Kong, Xinxin; Chen, Huifeng; Yu, Qiulin; Cai, Shuo; Huang, Yuanyuan; Du, Sichun (Frontiers in Physics, 2022)
This paper proposes a new memristor model and uses pinched hysteresis loops (PHL) to prove the memristor characteristics of the model. Then, a new fractional-order memristive Hopfield neural network (FMHNN) is presented by using this memristor to simulate the induced current, and the bifurcation characteristics and coexistence attractor characteristics of FMHNN is studied. Because this FMHNN has chaotic characteristics, we also uses this FMHNN to generate random number and apply it to the field of image encryption. We makes a series of analysis on the randomness of random numbers and the security of image encryption, and proves that the encryption algorithm using this FMHNN is safe and sensitive to the key.