Optimizing LSTM with multi-strategy improved WOA for robust prediction of high-speed machine tests data

LSTM networks are popular for predicting data with nonlinear and temporal properties. However, it is difficult to select optimal hyperparameters using empirical methods, which can significantly affect their performance and modeling time. To address this, we propose a novel hybrid model called CMAL-W...

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Veröffentlicht in:	Chaos, solitons and fractals Jg. 178; S. 114394
Hauptverfasser:	Che, Zhongyuan, Peng, Chong, Yue, Chenxiao
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.01.2024
Schlagworte:	Hyperparameter optimization Long short-term memory Milling force prediction Multi-strategy improved whale optimization algorithm Tool wear prediction Long short-term memory Hyperparameter optimization Multi-strategy improved whale optimization algorithm Milling force prediction Tool wear prediction
ISSN:	0960-0779
Online-Zugang:	Volltext
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