Open AccessApplications of Zeroing neural networks: A surveyOpen AccessAuthor(s)
Tinglei Wang,
Zhen Zhang,
Yun Huang,
Bolin Liao,
Shuai Li
Publication year2024
Publication title
ieee access
Resource typeMagazines
PublisherIEEE
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.
Subject(s)aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
Keyword(s)Time-varying systems, Neural networks, Robustness, Manipulators, Surveys, Recurrent neural networks, Real-time systems, Fluctuations, Robots, time-varying problems, Zeroing neural network (ZNN), robustness, robot manipulator
Language(s)English
SCImago Journal Rank0.587
H-Index127
eISSN2169-3536
DOI10.1109/access.2024.3382189
Seeing content that should not be on Zendy? Contact us.