scholarly journals An ensemble‐driven long short‐term memory model based on mode decomposition for carbon price forecasting of all eight carbon trading pilots in China

2020 ◽  
Vol 8 (11) ◽  
pp. 4094-4115
Author(s):  
Wei Sun ◽  
Zhaoqi Li
Keyword(s):  
Short Term Memory ◽  
Carbon Price ◽  
Memory Model ◽  
Price Forecasting ◽  
Carbon Trading ◽  
Short Term ◽  
Term Memory ◽  
Model Based ◽  
Mode Decomposition ◽  
Long Short Term Memory

scholarly journals Mold-level prediction based on long short-term memory model and multi-mode decomposition with mutual information entropy

2019 ◽  
Vol 11 (12) ◽  
pp. 168781401989443 ◽  
Author(s):  
Wenbin Su ◽  
Zhufeng Lei
Keyword(s):  
Mutual Information ◽  
Continuous Casting ◽  
Short Term Memory ◽  
Memory Model ◽  
Short Term ◽  
Term Memory ◽  
Dominant Component ◽  
Mode Decomposition ◽  
Level Data ◽  
Long Short Term Memory

The mold is referred to as the heart of the continuous casting machine. Mold-level control is one of the keys to ensuring the quality of a high-efficiency continuous casting slab. This article addresses the failure of the mold-level prediction model in the actual production process to overcome the impact of noise. To improve the accuracy of mold-level prediction, a novel method for mold-level prediction based on the multi-mode decomposition method and the long short-term memory model is proposed. First, empirical mode decomposition of the mold-level data is performed. The actual eigenmode component number K is obtained through the calculation of the mutual information entropy of the eigenmode components. Then, we perform a K-based variational mode decomposition on the mold-level data. The noise dominant component is denoised by the calculation of the mutual information entropy of the eigenmode components. Moreover, the long short-term memory model is used to predict the noise dominant component and the information dominant component after denoising. Finally, the predicted result is subjected to variational mode decomposition reconstruction to obtain the predicted mold-level data. The experimental results show that compared with the other methods tested, the model has better prediction efficiency, prediction accuracy, and generalization ability. It provides a new idea for mold-liquid-level prediction and continuous casting blank quality assurance.

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