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Design and analysis of grid attractors in memristive Hopfield neural networks

Author

Listed:
  • Yuan, Fang
  • Qi, Yaning
  • Yu, Xiangcheng
  • Deng, Yue

Abstract

This paper proposes three types of memristive Hopfield neural networks (M-HNNs) that incorporate connections comprising memristor self-synapses and unidirectional synapses. The M-HNNs are designed as simple three-neuron structures capable of configuring multi-double-scroll attractors in terms of both attractor directions and numbers by applying an external excitation current to neurons. Complex dynamic behaviors are investigated, including space multi-structure chaotic attractors and coexisting behaviors with space initial offset. Phase portraits, bifurcation diagrams, and Lyapunov exponents are employed to reveal and examine the specific dynamics. Furthermore, microcontroller-based digital hardware platforms are utilized to validate the numerical simulations, and the practicality of the proposed M-HNNs is demonstrated through the design of three corresponding pseudo-random number generators (PRNGs).

Suggested Citation

  • 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).
    • Handle: RePEc:eee:chsofr:v:188:y:2024:i:c:s0960077924010300
    DOI: 10.1016/j.chaos.2024.115478
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