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August 21, 2025Journal of Nonlinear Complex and Data Science

Multiple attractors and chaos synchronization of memristor-based Hopfield neural networks

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Authors

QCQun ChenNorth China University of TechnologyXZXianhe ZhangHubei Normal UniversityJLJianhao LiChina University of Geosciences

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Overview

This analysis demonstrates fixed-time chaos synchronization in memristor-based neural networks, suggesting innovative applications in image encryption.

Key Points

  • Achieving fixed-time synchronization in memristor-based Hopfield neural networks shows promise for enhanced dynamic behavior, including multistability.
  • The model effectively demonstrates the coexistence of single- and double-scroll attractors, indicating its complex dynamic characteristics.
  • Circuit construction validated the theoretical model, emphasizing the practical implications of memristor technology in neural networks.
  • This work supports the development of control systems, potentially influencing image encryption techniques and other applications.

Cite This Study

Chen et al. (2025) studied this question.

synapsesocial.com/papers/68af5218ad7bf08b1ead99abhttps://doi.org/10.1515/jncds-2024-0063
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