Time-Series Prediction of Iron and Silicon Content in Aluminium Electrolysis Based on Machine Learning

In analyzing dynamic characteristic of time-series data, classic prediction models rely heavily on static historical data, and tacit knowledge is difficult to be mined effectively. Therefore, a hybrid prediction model GS-GMDH is proposed based on growing neural gas (GNG) and the group method of data...

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Bibliographic Details

Published in:	IEEE Access
Main Authors:	Linsheng Chen, Yongming Wu, Yingbo Liu, Tiansong Liu, Xiaojing Sheng
Format:	Article
Language:	English
Published:	IEEE 2021-01-01
Subjects:	Contents of iron and silicon machine learning aluminium electrolysis GNG GMDH
Online Access:	https://ieeexplore.ieee.org/document/9319140/