An Adaptive Gradient Neural Network to Solve Dynamic Linear Matrix Equations

In this article, the existing approaches, including numerical algorithms as well as neural networks to solve dynamic linear matrix equations, have been presented and reviewed. Specifically, the conventional gradient recurrent neural networks (CGRNNs) and the conventional zeroing neural networks (CZN...

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Vydané v:	IEEE transactions on systems, man, and cybernetics. Systems Ročník 52; číslo 9; s. 5913 - 5924
Hlavní autori:	Liao, Shan, Liu, Jiayong, Qi, Yimeng, Huang, Haoen, Zheng, Rongfeng, Xiao, Xiuchun
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.09.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Adaptation models Adaptive gradient recurrent neural networks (AGRNNs) Algorithms angle-of-arrival (AoA) localization Computational modeling Convergence dynamic linear matrix equations Heuristic algorithms inversion-free Load modeling Mathematical models Motion planning Neural networks Numerical models Recurrent neural networks Robot dynamics
ISSN:	2168-2216, 2168-2232
On-line prístup:	Získať plný text
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