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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Bibliographic Details

Published in:	Chaos, solitons and fractals Vol. 178; p. 114394
Main Authors:	Che, Zhongyuan, Peng, Chong, Yue, Chenxiao
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 01.01.2024
Subjects:	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 Access:	Get full text
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