A dual-neuron memristive hopfield neural network and its application in image encryption

As one of the four basic electronic components, memristors have been widely applied in artificial neural networks in recent years. However, most of the existing researches focus on the application of memristors in artificial neural networks with three or more neurons, and the artificial neural networks with two neurons are rarely reported. Therefore, a one equilibrium point dual-neuron memristive Hopfield neural network (DNMHNN) model with a memristor serving as the coupling synapse between neurons is proposed in this work. In this research, the chaotic dynamic characteristics of the proposed DNMHNN model are analyzed by various fundamental dynamic analysis methods. Then, its chaotic dynamic characteristics are further validated by field programmable gate array implementation, which further verifies the consistency of the theoretical analysis and experimental results. Finally, a color image encryption algorithm based on the chaotic sequences generated by the proposed DNMHNN model is designed and implemented, which further validates the practical application feasibility and security of the proposed DNMHNN model and the image encryption algorithm.