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A novel memristive synapse-coupled ring neural network with countless attractors and its application

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  • Zhang, Sen
  • Li, Yongxin
  • Lu, Daorong
  • Li, Chunbiao

Abstract

This paper presents a novel memristive synapse-coupled ring neural network (MSCRNN) through introducing a nonvolatile memristor into a three-neuron Hopfield neural network connected by a unidirectional ring topology. Complex dynamics relying on control parameters and initial states is thoroughly explored using numerical analysis techniques. Numerical analyses show that the MSCRNN not only exhibits bistability, tristability, but also in particular evolves an intriguing phenomenon known as homogeneous multistability, characterized by the emergence of an infinite number of homogeneous coexisting attractors triggered by the memristor initial states. In addition, a hardware test platform based on the CH32 microcontroller is built to experimentally validate these numerical findings. Finally, a new pseudorandom number generator is developed taking advantage of memristor initial-regulated chaotic sequences derived from the MSCRNN. Performance analysis outcomes indicate that these chaotic sequences possess the capability to generate pseudorandom numbers demonstrating exceptional randomness, rendering them highly advantageous for utilization in various chaos-based engineering applications.

Suggested Citation

  • Zhang, Sen & Li, Yongxin & Lu, Daorong & Li, Chunbiao, 2024. "A novel memristive synapse-coupled ring neural network with countless attractors and its application," Chaos, Solitons & Fractals, Elsevier, vol. 184(C).
    • Handle: RePEc:eee:chsofr:v:184:y:2024:i:c:s0960077924006088
    DOI: 10.1016/j.chaos.2024.115056
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    References listed on IDEAS

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    1. Deng, Quanli & Wang, Chunhua & Lin, Hairong, 2024. "Memristive Hopfield neural network dynamics with heterogeneous activation functions and its application," Chaos, Solitons & Fractals, Elsevier, vol. 178(C).
    2. Dong, Yujiao & Yang, Shuting & Liang, Yan & Wang, Guangyi, 2022. "Neuromorphic dynamics near the edge of chaos in memristive neurons," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).
    3. Suhas Kumar & John Paul Strachan & R. Stanley Williams, 2017. "Chaotic dynamics in nanoscale NbO2 Mott memristors for analogue computing," Nature, Nature, vol. 548(7667), pages 318-321, August.
    4. Ding, Shoukui & Wang, Ning & Bao, Han & Chen, Bei & Wu, Huagan & Xu, Quan, 2023. "Memristor synapse-coupled piecewise-linear simplified Hopfield neural network: Dynamics analysis and circuit implementation," Chaos, Solitons & Fractals, Elsevier, vol. 166(C).
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    Cited by:

    1. Yuan, Fang & Qi, Yaning & Yu, Xiangcheng & Deng, Yue, 2024. "Design and analysis of grid attractors in memristive Hopfield neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 188(C).
    2. Yuzhou Xi & Yu Ning & Jie Jin & Fei Yu, 2024. "A Dynamic Hill Cipher with Arnold Scrambling Technique for Medical Images Encryption," Mathematics, MDPI, vol. 12(24), pages 1-22, December.
    3. Yangxin Luo & Yuanyuan Huang & Fei Yu & Diqing Liang & Hairong Lin, 2024. "Adaptive Asymptotic Shape Synchronization of a Chaotic System with Applications for Image Encryption," Mathematics, MDPI, vol. 13(1), pages 1-18, December.

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