Scholarlink

<p>The complexity of neural dynamics heavily depends on the nonlinear activation functions, and a mixed-bipower activation function (MBPAF) with adjustable parameters is designed for the memristive Hopfield neural network (MHNN) to generate complex hyper-chaotic behaviors. Based on the designed MBPAF, a novel MBPAF-memristive Hopfield neural network (MBPAF-MHNN) model is proposed. The complex dynamics of the proposed MBPAF-MHNN model are validated through numerical analyses and further verified via FPGA implementation. Finally, a robust image encryption scheme is designed based on the MBPAF-MHNN model, featuring a plaintext-related “Diffusion-Permutation-Diffusion" architecture with DNA-based operations.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A MBPAF-memristive Hopfield neural network and its application in image encryption

  • 期刊论文

  • 发表日期：

    2026年4月15日

查看全文
  • Shilong Deng,
  • Jie Jin,
  • Zhijing Li,
  • Chaoyang Chen,
  • Fei Yu

摘要

The complexity of neural dynamics heavily depends on the nonlinear activation functions, and a mixed-bipower activation function (MBPAF) with adjustable parameters is designed for the memristive Hopfield neural network (MHNN) to generate complex hyper-chaotic behaviors. Based on the designed MBPAF, a novel MBPAF-memristive Hopfield neural network (MBPAF-MHNN) model is proposed. The complex dynamics of the proposed MBPAF-MHNN model are validated through numerical analyses and further verified via FPGA implementation. Finally, a robust image encryption scheme is designed based on the MBPAF-MHNN model, featuring a plaintext-related “Diffusion-Permutation-Diffusion" architecture with DNA-based operations.