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Generation and control of grid multi-vortex attractors in memristive Hopfield neural network

Author

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  • Huang, Lilian
  • Chen, Fangyi
  • Geng, Feiyi
  • Zheng, Lei
  • Yu, Xihong

Abstract

The grid multi-vortex attractors previously generated have typically been derived by integrating the multi-piecewise nonlinear magnetron memristor model into neural network frameworks, with limited exploration of alternative operational mechanisms. To solve this problem, a method for constructing meshed multi-vortex attractors using a memristor-based Hopfield neural network is introduced by this paper. Firstly, a trineuron-based memristor Hopfield neural network is proposed, which can generate and regulate multi-vortex attractors. At the same time, the influence of multilayer logic pulses on the dynamics of the memristor-based Hopfield neural network is focused on by this paper, and it discusses how different pulse modes regulate the attractor state of the network, thereby revealing the profound influence of pulse regulation on network behavior. In addition, the migration control behavior of the multi-vortex attractor and the grid multi-vortex attractor is studied from multiple dimensions. Ultimately, by developing an analog circuit, the numerical simulation results of the MHNN with multi-level logic pulses were replicated. The simulation results indicate the feasibility of implementing the method based on hardware.

Suggested Citation

  • Huang, Lilian & Chen, Fangyi & Geng, Feiyi & Zheng, Lei & Yu, Xihong, 2025. "Generation and control of grid multi-vortex attractors in memristive Hopfield neural network," Chaos, Solitons & Fractals, Elsevier, vol. 199(P2).
    • Handle: RePEc:eee:chsofr:v:199:y:2025:i:p2:s0960077925007301
    DOI: 10.1016/j.chaos.2025.116717
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    References listed on IDEAS

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    1. Yang, GuiJiang & Ai, Hao & Liu, Wei & Wang, Qiubao, 2023. "Weak signal detection based on variable-situation-potential with time-delay feedback and colored noise," Chaos, Solitons & Fractals, Elsevier, vol. 169(C).
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