Scholarlink

Memristive neural networks (MNNs) have emerged as a promising paradigm for neuromorphic computing, combining the unique properties of memristors with the powerful computational capabilities of neural networks. This chapter presents a comprehensive analysis of the dynamic behavior and stability of MNNs under various conditions, including stochastic disturbances, time-varying delays, fuzzy switching, and connection faults. This chapter explores novel control strategies and analytical methods to address the challenges associated with these conditions and ensure the reliable operation of MNNs.

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

Dynamic Analysis of Memristive Neural Network

  • 章节

  • 发表日期：

    2025年

查看全文
  • Yongbin Yu,
  • Xiangxiang Wang,
  • Xiao Feng,
  • Jiarun Shen,
  • Nyima Tashi,
  • Pinaki Mazumder

摘要

Memristive neural networks (MNNs) have emerged as a promising paradigm for neuromorphic computing, combining the unique properties of memristors with the powerful computational capabilities of neural networks. This chapter presents a comprehensive analysis of the dynamic behavior and stability of MNNs under various conditions, including stochastic disturbances, time-varying delays, fuzzy switching, and connection faults. This chapter explores novel control strategies and analytical methods to address the challenges associated with these conditions and ensure the reliable operation of MNNs.