Dual Memristive Hopfield Neural Networks: Bistable Coexistence Attractor And Image Encryption

This paper proposes a novel dual memristive Hopfield neural network (HNN) system which can generate coexisting attractor. The time domain waveform and hysteresis loop of two memristor are displayed. And the dynamic behavior is shown with different dual memristors’ parameters. Particularly, Based on the characteristic that the equilibrium point of system is symmetrical about the origin. We select two initial values which symmetrical about origin such as 0.1,0.1,0.1 and -0.1,-0.1,-0.1, find a pair of bistable coexistence attractor, and analyze their dynamic behavior such as coexisting bifurcation diagrams, LEs. And we design a new encryption process. It provides a new ideas for research on memristor coupled HNN.