Applications of Zeroing Neural Networks: A Survey
Wang, Tinglei; Zhang, Zhen; Huang, Yun; Liao, Bolin; Li, Shuai (2024-03-27)
IEEE
27.03.2024
T. Wang, Z. Zhang, Y. Huang, B. Liao and S. Li, "Applications of Zeroing Neural Networks: A Survey," in IEEE Access, vol. 12, pp. 51346-51363, 2024, doi: 10.1109/ACCESS.2024.3382189
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© 2024 The Authors. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
https://creativecommons.org/licenses/by/4.0/
© 2024 The Authors. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
https://creativecommons.org/licenses/by/4.0/
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202404262961
https://urn.fi/URN:NBN:fi:oulu-202404262961
Tiivistelmä
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.
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.
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