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Discrete memristor applied to construct neural networks with homogeneous and heterogeneous coexisting attractors

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  • Lai, Qiang
  • Yang, Liang

Abstract

Memristors are widely used to simulate the effects of electromagnetic radiation on neurons or as synapses to simulate excitation and inhibition between neurons. This paper constructs discrete neural network models (DNNMs) in three scenarios: without discrete memristor, discrete memristor simulate electromagnetic radiation stimulation, and discrete local active memristor simulate synapses. Research reveals that the DNNM without fixed points under electromagnetic radiation stimulation, which in turn induces hidden hyperchaotic attractors and also observes the existence of infinite coexisting homogeneous attractors. Meanwhile, the DNNM can generate heterogeneous coexisting attractors after introducing a memristor to mimic synapses. Numerical results show that discrete memristors can induce the DNNM to develop more complex chaotic dynamics. In addition, a hardware platform is designed to validate the numerical results, and the DNNMs are applied to construct the pseudo-random number generator (PRNG).

Suggested Citation

  • 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).
    • Handle: RePEc:eee:chsofr:v:174:y:2023:i:c:s0960077923007087
    DOI: 10.1016/j.chaos.2023.113807
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    References listed on IDEAS

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    1. Lai, Qiang & Yang, Liang & Liu, Yuan, 2022. "Design and realization of discrete memristive hyperchaotic map with application in image encryption," Chaos, Solitons & Fractals, Elsevier, vol. 165(P1).
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    6. Wan, Qiuzhen & Li, Fei & Chen, Simiao & Yang, Qiao, 2023. "Symmetric multi-scroll attractors in magnetized Hopfield neural network under pulse controlled memristor and pulse current stimulation," Chaos, Solitons & Fractals, Elsevier, vol. 169(C).
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    8. Muni, Sishu Shankar & Rajagopal, Karthikeyan & Karthikeyan, Anitha & Arun, Sundaram, 2022. "Discrete hybrid Izhikevich neuron model: Nodal and network behaviours considering electromagnetic flux coupling," Chaos, Solitons & Fractals, Elsevier, vol. 155(C).
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    Cited by:

    1. Yang, Jinrong & Chen, Guici & Wen, Shiping & Wang, Leimin, 2023. "Finite-time dissipative control for discrete-time memristive neural networks via interval matrix method," Chaos, Solitons & Fractals, Elsevier, vol. 176(C).
    2. Jia, Junen & Wang, Chunni & Zhang, Xiaofeng & Zhu, Zhigang, 2024. "Energy and self-adaption in a memristive map neuron," Chaos, Solitons & Fractals, Elsevier, vol. 182(C).
    3. Qin, Bo & Zhang, Ying, 2024. "Comprehensive analysis of the mechanism of sensitivity to initial conditions and fractal basins of attraction in a novel variable-distance magnetic pendulum," Chaos, Solitons & Fractals, Elsevier, vol. 183(C).

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