Applications of Zeroing Neural Networks: A Survey

Tinglei Wang, Zhen Zhang, Yun Huang, Bolin Liao, Shuai Li

Research output: Contribution to journalArticleScientificpeer-review

Abstract

Time-varying problems are prevalent in engineering, presenting a significant challenge due to the fluctuations in parameters and goals at different time points. The zeroing neural network (ZNN), a specialized form of recurrent neural network (RNN) developed by Zhang et al., has gained attention for its rapid convergence speed and robustness making it a valuable tool for real-time solving of diverse time-varying problems. This review article explores the practical applications of ZNN across various domains in the past two decades, specifically focusing on robot manipulator path tracking, motion planning, and chaotic systems. The comprehensive scope of this review is essential for researchers and beginners looking to grasp the efficacy of ZNN in addressing practical challenges in diverse fields.

Original languageEnglish
Pages (from-to)51346-51363
Number of pages18
JournalIEEE Access
Volume12
DOIs
Publication statusPublished - 2024
MoE publication typeA1 Journal article-refereed

Keywords

  • robot manipulator
  • robustness
  • Time-varying problems
  • zeroing neural network (ZNN)

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