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March 7, 2026International Journal of Bifurcation and Chaos

Hopfield Neural Network Dynamics with Dual Memristor Unidirectional Synaptic Connections and Its Application

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Authors

MFManhong FanSXShi-Qi XuYBYong-Long Bai

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Overview

Investigates a novel memristor-based Hopfield neural network for image encryption, suggesting enhanced dynamic properties and security.

Key Points

  • The aim is to analyze the dynamic properties of a Two-Sinusoidal Memristor Hopfield Neural Network (TSM-HNN) and its application in image encryption.
  • Proposed a Two-Sinusoidal Memristor Hopfield Neural Network (TSM-HNN) using two sinusoidal memristors as synaptic weights.
  • Performed dynamic analysis of unidirectional synaptic connections and stability on the initial state plane.
  • Employed numerical simulations to investigate complex behaviors and state transitions under varying conditions.
  • Designed a hardware circuit based on STM32 microcontroller to validate numerical results.
  • Demonstrated coexisting attractors under the same conditions with different initial states.
  • Revealed stability features and planar equilibrium sets in the TSM-HNN model.
  • Validated image encryption capabilities and assessed security performance through various tests.

Cite This Study

Fan et al. (2026) studied this question.

synapsesocial.com/papers/69abc2725af8044f7a4ec1fehttps://doi.org/10.1142/s0218127426501014
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Also Consider

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  1. 1Memristor-coupled dynamics and synchronization in two bi-neuron Hopfield neural networks2025
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  3. 3Multistable Memristor Synapse-Based Coupled Bi-Hopfield Neuron Model: Dynamic Analysis, Microcontroller Implementation and Image Encryption2024 · 7 citations
  4. 4Multi-Scroll Hopfield Neural Network Excited by Memristive Self-Synapses and Its Application in Image Encryption2025 · 40 citations
  5. 5Multi-mechanism driven geometric control of discrete memristive dual-neuron HNN: Modulation analysis and hardware implementation2025 · 4 citations