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Design and circuit implementation of a novel 5D memristive CNN hyperchaotic system

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  • Xiu, Chunbo
  • Fang, Jingyao
  • Liu, Yuxia

Abstract

In order to enrich the dynamic characteristics of cellular neural network (CNN) and reveal the influence of memristor nonlinearity on the dynamic behavior of the network, a novel five-dimension memristive cellular neural network hyperchaotic system is designed by replacing the linear resistors in the output module of cellular neural network with two flux-controlled memristors, and the hardware circuit design of the system is completed. Based on Lyapunov exponent spectrum and attractor phase trajectories, the effects of system parameters and initial value on the dynamic characteristics of the memristive cellular neural network model are studied, and the generation conditions of different chaotic attractors and coexisting chaotic attractors are explained. Kolmogorov entropy is used to measure the chaotic degree of the system when the parameters are in different intervals. In this way, the criteria of parameter selection for system application are given. In particular, the effect of Gaussian white noise on the dynamic behavior of memristive CNN chaotic system is studied. The hardware circuit design and characteristic analysis of memristive cellular neural network are completed by the circuit simulation software, and the physical realizability of chaotic characteristics of the memristive cellular neural network model is verified. Furthermore, a secure communication application example based on the hyperchaotic system is given.

Suggested Citation

  • Xiu, Chunbo & Fang, Jingyao & Liu, Yuxia, 2022. "Design and circuit implementation of a novel 5D memristive CNN hyperchaotic system," Chaos, Solitons & Fractals, Elsevier, vol. 158(C).
    • Handle: RePEc:eee:chsofr:v:158:y:2022:i:c:s0960077922002508
    DOI: 10.1016/j.chaos.2022.112040
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    References listed on IDEAS

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    1. Dmitri B. Strukov & Gregory S. Snider & Duncan R. Stewart & R. Stanley Williams, 2008. "The missing memristor found," Nature, Nature, vol. 453(7191), pages 80-83, May.
    2. Fu, Qianhua & Zhong, Shouming & Shi, Kaibo, 2021. "Exponential synchronization of memristive neural networks with inertial and nonlinear coupling terms: Pinning impulsive control approaches," Applied Mathematics and Computation, Elsevier, vol. 402(C).
    3. Lin, Hairong & Wang, Chunhua, 2020. "Influences of electromagnetic radiation distribution on chaotic dynamics of a neural network," Applied Mathematics and Computation, Elsevier, vol. 369(C).
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    Cited by:

    1. Liang, Bo & Hu, Chenyang & Tian, Zean & Wang, Qiao & Jian, Canling, 2023. "A 3D chaotic system with multi-transient behavior and its application in image encryption," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 616(C).
    2. Lai, Qiang & Yang, Liang, 2023. "Discrete memristor applied to construct neural networks with homogeneous and heterogeneous coexisting attractors," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).
    3. Jie Zhang & Enze Liu, 2024. "Circuit design and image encryption of CNN chaotic system based on memristor," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 97(7), pages 1-19, July.

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