期刊
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
卷 18, 期 8, 页码 5297-5306出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2021.3119387
关键词
Memristors; Couplings; Mathematical models; Dynamical systems; Complexity theory; Three-dimensional displays; Solid modeling; Auxiliary classifier generative adversarial nets (AC-GANs); chaos complexity; discrete map; discrete memristor (DM); hardware implementation; hyperchaotic sequence
类别
资金
- National Natural Science Foundation of China [62071142, 51777016, TII-21-3629]
This article presents a three-dimensional discrete memristor-based (3-D-DM) map model and simulates its bifurcation behaviors using numerical measures. The results show that the memristor can enhance the chaos complexity of existing discrete maps and its coupling maps can display hyperchaos. Furthermore, a hardware platform is developed to implement the 3-D-DM maps and the acquired hyperchaotic sequences have high randomness, which can greatly improve the discriminator accuracy of auxiliary classifier generative adversarial nets.
With the nonlinearity and plasticity, memristors are widely used as nonlinear devices for chaotic oscillations or as biological synapses for neuromorphic computations. But discrete memristors (DMs) and their coupling maps have not received much attention, yet. Using a DM model, this article presents a general three-dimensional discrete memristor-based (3-D-DM) map model. By coupling the DM with four 2-D discrete maps, four examples of 3-D-DM maps with no or infinitely many fixed points are generated. We simulate the coupling coefficient-depended and memristor initial-boosted bifurcation behaviors of these 3-D-DM maps using numerical measures. The results demonstrate that the memristor can enhance the chaos complexity of existing discrete maps and its coupling maps can display hyperchaos. Furthermore, a hardware platform is developed to implement the 3-D-DM maps and the acquired hyperchaotic sequences have high randomness. Particularly, these hyperchaotic sequences can be applied to the auxiliary classifier generative adversarial nets for greatly improving the discriminator accuracy.
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