Abstract
Neural networks are favored by academia and industry because of their diversity of dynamics. However, it is difficult for ring neural networks to generate complex dynamical behaviors due to their special structure. In this paper, we present a memristive ring neural network (MRNN) with four neurons and one non-ideal flux-controlled memristor. The memristor is used to describe the effect of external electromagnetic radiation on neurons. The chaotic dynamics of the MRNN is investigated in detail by employing phase portraits, bifurcation diagrams, Lyapunov exponents and attraction basins. Research results show that the MRNN not only can generate abundant chaotic and hyperchaotic attractors but also exhibits complex multistability dynamics. Meanwhile, an analog MRNN circuit is experimentally implemented to verify the numerical simulation results. Moreover, a medical image encryption scheme is constructed based on the MRNN from a perspective of practical engineering application. Performance evaluations demonstrate that the proposed medical image cryptosystem has several advantages in terms of keyspace, information entropy and key sensitivity, compared with cryptosystems based on other chaotic systems. Finally, hardware experiment using the field-programmable gate array (FPGA) is carried out to verify the designed cryptosystem.
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Acknowledgements
This work is supported by the Major Research Project of the National Natural Science Foundation of China (91964108), the National Natural Science Foundation of China (61971185, 62101182), The Natural Science Foundation of Hunan Province (2020JJ4218), the China Postdoctoral Science Foundation (2020M682552) and the Scientific Research Project of Hunan Provincial Department of Education (21C0200).
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Lin, H., Wang, C., Cui, L. et al. Hyperchaotic memristive ring neural network and application in medical image encryption. Nonlinear Dyn 110, 841–855 (2022). https://doi.org/10.1007/s11071-022-07630-0
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DOI: https://doi.org/10.1007/s11071-022-07630-0