2022
DOI: 10.1016/j.neucom.2021.10.012
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Closed-LSTM neural network based reference modification for trajectory tracking of piezoelectric actuator

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Cited by 13 publications
(4 citation statements)
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“…These result can be compared with the ones obtained by the authors of Ref. [ 57 ], who obtained maximum modeling errors of 0.35 m for a NARX and 0.24 micro for a LSTM-NN network.…”
Section: Resultssupporting
confidence: 54%
“…These result can be compared with the ones obtained by the authors of Ref. [ 57 ], who obtained maximum modeling errors of 0.35 m for a NARX and 0.24 micro for a LSTM-NN network.…”
Section: Resultssupporting
confidence: 54%
“…31,32 The LSTM structure includes three gates: the forget gate, the input gate, and the output gate, which work together to form the loop module of the network. 33,34 Each gate has a specic function:…”
Section: Deep Belief Networkmentioning
confidence: 99%
“…Recognizing these challenges, recent decades have witnessed the integration of neural networks in hysteresis modeling of piezoelectric dynamic systems [13][14][15].…”
Section: Introductionmentioning
confidence: 99%