A Novel Memristive Multiscroll Multistable Neural Network With Application to Secure Medical Image Communication
Sen Zhang, Xuenan Peng, Xiaoping Wang, Chengjie Chen, Zhigang Zeng
Abstract
Owing to their ability to effectively characterize the memory effect of magnetic flux, specifically in relation to the effect of external electromagnetic radiation, memristors have elicited widespread interest in the construction of neural networks with complex dynamics. This work proposes a novel memristive multiscroll multistable neural network (MMSMSNN), wherein multistable threshold memristors are used to describe external electromagnetic radiation effects. Numerical simulations show that the MMSMSNN can yield any number of cubic lattice multiscroll attractors by adjusting the internal parameters of memristors. Another highlight is that it can also be able to yield abundant initial offset boosting behaviors, i.e., different kinds of infinitely many homogeneous coexisting attractors, including linearly arranged homogeneous coexisting attractors, planar lattice-distributed homogeneous coexisting attractors, and cubic lattice-distributed homogeneous coexisting attractors. In addition, hardware experiments based on the CH32V307 microcontroller are carried out to demonstrate the numerical findings. Finally, a new secure medical image communication scheme is designed to investigate the MMSMSNN in practical applications, and performance analyses reveal its superiority and high security.