A Class of Discrete Memristive Hyperchaotic Maps With Multicavity Multistructure Attractors and Its Application in Secure Communication

Memristors with nonlinearity and memory characteristics can effectively enhance chaotic dynamics complexity for chaotic maps. In this work, we present a novel discrete memristor model and couple it with sine maps and iterative chaotic maps with infinite collapse (ICMIC) to construct a class of discrete memristive hyperchaotic maps with multicavity multistructure attractors. This class of multicavity multistructure memristive sine ICMIC modulation maps (MCMS-MSIMMs) possesses an infinite variety of configurations, where the quantity and position of coupled discrete memristors can be arbitrarily combined. Numerical simulation results demonstrate that the sample map can exhibit hyperchaos, nondegeneracy, large-scale parameter control, multicavity attractors, multistructure attractors, and multicavity multistructure attractors. The complexity and initial values of the system are explored, revealing the high permutation entropy and initial offset-boosting behavior. In addition, the field programmable gate array (FPGA)-based MCMS-MSIMM hardware circuit is designed, and the experimental results are consistent with the numerical results. Finally, MCMS-MSIMM is applied in secure communication, and the experimental results indicate that the proposed map has better noise resistance performance compared to existing maps.