Star Memristive Neural Network: Dynamics Analysis, Circuit Implementation, and Application in a Color Cryptosystem

被引:2
作者
Fu, Sen [1 ,2 ,3 ]
Yao, Zhengjun [1 ]
Qian, Caixia [1 ,2 ]
Wang, Xia [2 ]
机构
[1] Nanjing Univ Aeronaut & Astronaut, Coll Mat Sci & Technol, Nanjing 211100, Peoples R China
[2] Hunan Aerosp Co Ltd, Aircraft Technol Branch, Changsha 410000, Peoples R China
[3] China Aerosp Sci & Ind Corp, Beijing 100048, Peoples R China
关键词
memristor; Hopfield neural network; multi-scroll attractors; initial boosting behavior; circuit implementation; image encryption; HYPERCHAOS; ATTRACTORS; BRAIN;
D O I
10.3390/e25091261
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
At present, memristive neural networks with various topological structures have been widely studied. However, the memristive neural network with a star structure has not been investigated yet. In order to investigate the dynamic characteristics of neural networks with a star structure, a star memristive neural network (SMNN) model is proposed in this paper. Firstly, an SMNN model is proposed based on a Hopfield neural network and a flux-controlled memristor. Then, its chaotic dynamics are analyzed by using numerical analysis methods including bifurcation diagrams, Lyapunov exponents, phase plots, Poincare maps, and basins of attraction. The results show that the SMNN can generate complex dynamical behaviors such as chaos, multi-scroll attractors, and initial boosting behavior. The number of multi-scroll attractors can be changed by adjusting the memristor's control parameters. And the position of the coexisting chaotic attractors can be changed by switching the memristor's initial values. Meanwhile, the analog circuit of the SMNN is designed and implemented. The theoretical and numerical results are verified through MULTISIM simulation results. Finally, a color image encryption scheme is designed based on the SMNN. Security performance analysis shows that the designed cryptosystem has good security.
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页数:17
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