Star Memristive Neural Network: Dynamics Analysis, Circuit Implementation, and Application in a Color Cryptosystem
Author:
Fu Sen123, Yao Zhengjun1, Qian Caixia12, Wang Xia2
Affiliation:
1. College of Materials Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211100, China 2. Aircraft Technology Branch of Hunan Aerospace Co., Ltd., Changsha 410000, China 3. China Aerospace Science and Industry Corporation, Beijing 100048, China
Abstract
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, Poincaré 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.
Funder
Group company integrated innovation projects
Subject
General Physics and Astronomy
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