A Memristive Synapse Control Method to Generate Diversified Multi-Structure Chaotic Attractors

Due to the synapse-like nonlinearity and memory characteristics, memristor is often used to construct memristive neural networks with complex dynamical behaviors. However, memristive neural networks with multi-structure chaotic attractors have not been found yet. In this paper, a novel method for designing multi-structure chaotic attractors in memristive neural networks is proposed. By utilizing a multi-piecewise memristive synapse control in a Hopfield neural network (HNN), various complex multi-structure chaotic attractors can be produced. Theoretical analysis and numerical simulation demonstrate that multiple multi-structure chaotic attractors with different topologies can be generated by conducting the memristive synapse-control in different synaptic coupling positions. Differing from traditional multi-scroll attractors, the generated multi-structure attractors contain multiple irregular shapes instead of simple scrolls. Meanwhile, the number of structures can be easily controlled with the memristor control parameters. Furthermore, we design a module-based analog memristive neural network circuit and the arbitrary number of multi-structure attractors can be obtained by selecting corresponding control voltages. Finally, based on the memristive HNNs, a novel image encryption cryptosystem with a permutation-diffusion structure is designed and evaluated, exhibiting its excellent encryption performances, especially the extremely high key sensitivity.