This study aims to improve the complexity of chaotic systems and the security accuracy of information encrypted transmission. Applying five-dimensional memristive Hopfield neural network (5D-HNN) to secure communication will greatly improve the confidentiality of signal transmission and greatly enhance the anticracking ability of the system.
Chaos masking: Chaos masking is the process of superimposing a message signal directly into a chaotic signal and masking the signal using the randomness of the chaotic output. Synchronous coupling: The coupled synchronization method first replicates the drive system to get the response system, and then adds the appropriate coupling term between the drive The synchronization error and the coupling term of the system will eventually converge to zero with time. The synchronization error and coupling term of the system will eventually converge to zero over time.
A 5D memristive neural network is obtained based on the original four-dimensional memristive neural network through the feedback control method. The system has five equations and contains infinite balance points. Compared with other systems, the 5D-HNN has rich dynamic behaviors, and the most unique feature is that it has multistable characteristics. First, its dissipation property, equilibrium point stability, bifurcation graph and Lyapunov exponent spectrum are analyzed to verify its chaotic state, and the system characteristics are more complex. Different dynamic characteristics can be obtained by adjusting the parameter k.
A new 5D memristive HNN is proposed and used in the secure communication
This work was supported by the key scientific research project of Hunan Provincial Department of Education (21A0317, 18A195) and the Natural Science Foundation of Hunan Province in China (2022JJ30266, 2019JJ50183).
Yin, X., Chen, J., Yu, W., Huang, Y., Wei, W., Xiang, X. and Yan, H. (2022), "Five-dimensional memristive Hopfield neural network dynamics analysis and its application in secure communication", Circuit World, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/CW-05-2022-0135
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