Initial-offset-boosted coexisting hyperchaos in a 2D memristive Chialvo neuron map and its application in image encryption

被引:0
作者
Quan Xu
Liping Huang
Ning Wang
Han Bao
Huagan Wu
Mo Chen
机构
[1] Changzhou University,School of Microelectronics and Control Engineering
来源
Nonlinear Dynamics | 2023年 / 111卷
关键词
Memristive Chialvo neuron map; Hyperchaotic dynamics; Initial-offset boosting behavior; Hardware experiment; Image encryption;
D O I
暂无
中图分类号
学科分类号
摘要
Designing a low-dimensional discrete map to generate chaotic sequence with the properties of high randomness and without chaos degradation is an attractive but challenging issue. The hyperchaotic dynamics of a low-dimensional discrete map can effectively solve this issue. To hit the issue, this paper introduces a memristor with sinusoidal mem-conductance function and hyper-tangent function modulated input into the one-dimensional (1D) Chialvo neuron map, thereby builds a two-dimensional (2D) memristive Chialvo neuron map with hyperchaotic dynamics. The stability and dynamical behaviors are disclosed by theoretical analysis and numerical simulation. Interestingly, the 2D memristive Chialvo neuron map can generate initial-offset boosting behavior. The forming mechanism of initial-offset boosting behavior is theoretically investigated. The results show that homogenous coexisting chaotic/hyperchaotic attractors can be regulated along memristor variable axis and boosted with the same periodicity of the sine mem-conductance. Besides, the homogenous coexisting chaotic/hyperchaotic attractors are experimentally captured on a FPGA-based digital platform. What’s more, a new encrypt algorithm is designed by employing the hyperchaotic sequences to encrypt an image. The multiple indicators and National Institute of Standards and Technology (NIST) test results show that the hyperchaotic sequences and encryption algorithm display good performance in image encryption.
引用
收藏
页码:20447 / 20463
页数:16
相关论文
共 302 条
  • [1] Naoki A(2023)Analysis of temporal structure of laser chaos by Allan variance Phys. Rev. E 107 1569-1578
  • [2] Nicolas C(2017)A review for dynamics in neuron and neuronal network Nonlinear Dyn. 89 109-129
  • [3] André R(2023)Biophysical neurons, energy and synapse controllability, a review J. Zhejiang Univ. Sci. A 24 84-91
  • [4] Kazutaka K(2020)Bifurcations to bursting and spiking in the Chay neuron and their validation in a digital circuit Chaos Solitons Fractals 141 2995-3010
  • [5] Atsushi U(2016)Complex bifurcations in the oscillatory reaction model Chaos Solitons Fractals 87 363-375
  • [6] Tomoaki N(2018)Dynamics analysis and Hamilton energy control of a generalized Lorenz system with hidden attractor Nonlinear Dyn. 94 1019-1027
  • [7] Satoshi S(2017)On the dynamics of Chua’s oscillator with smooth cubic nonlinearity: occurrence of multiple attractors Nonlinear Dyn. 87 58-70
  • [8] Ryoichi H(2018)Local sensitivity of spatiotemporal structures Nonlinear Dyn. 94 9509-9535
  • [9] Makoto N(2023)A color image encryption algorithm based on hyperchaotic map and DNA mutation Chin. Phys. B 32 8839-8850
  • [10] Ma J(2023)Dynamics analysis, FPGA realization and image encryption application of a 5D memristive exponential hyperchaotic system Integration 90 143-152