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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Veröffentlicht in:IEEE transactions on systems, man, and cybernetics. Systems Jg. 52; H. 9; S. 5913 - 5924
Hauptverfasser: Liao, Shan, Liu, Jiayong, Qi, Yimeng, Huang, Haoen, Zheng, Rongfeng, Xiao, Xiuchun
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.09.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
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
Online-Zugang:Volltext
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Zusammenfassung: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 (CZNNs) are successively provided to solve the dynamic problems and linear matrix equations, both of which manifest inherent limitations during the solving procedures. To remedy the drawbacks on convergence time, nonzero residual error, and large computational load of the traditional models, an adaptive gradient recurrent neural network (AGRNN) to solve dynamic linear matrix equations is proposed. This proposed inversion-free model possesses rapid convergence rate and accurate calculated solutions. Moreover, theoretical analyses guarantee the advantages of the AGRNN compared with the CGRNN and the CZNN to solve dynamic linear matrix equations. Finally, three numerical experiments, and applications to a PUMA 560 robot motion planning and a mobile subject localization based on angle-of-arrival technique are implemented to testify the advantages of the AGRNN.
Bibliographie:ObjectType-Article-1
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ISSN:2168-2216
2168-2232
DOI:10.1109/TSMC.2021.3129855