Complex production process prediction model based on EMD-XGBOOST-RLSE
| Autor: | Lintao Yan, Mushu Wang, Zheng-Guang Xu |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Basis (linear algebra)
Mean squared error Noise (signal processing) Computer science Mode (statistics) 02 engineering and technology Hilbert–Huang transform Data modeling Set (abstract data type) 020204 information systems Test set 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Algorithm |
| Zdroj: | 2017 9th International Conference on Modelling, Identification and Control (ICMIC). |
| DOI: | 10.1109/icmic.2017.8321591 |
| Popis: | In the production process, the complex conditions which have a lot of disturbance, noise and random fluctuations, greatly affected the accuracy of modeling. To slove this problem, a combined forecasting model based on empirical modal decomposition(EMD), extreme gradient boosting (XGBOOST), regularized least squares estimation (RLSE) is proposed. In this paper, the actual data of the sintering process is taken as an example to illustrate the construction process of the model. Firstly, the intrinsic mode functions(IMFs) of bellows temperature is obtained by empirical mode decomposition(EMD). Then, the XGBOOST model and RLSE model of each IMF are respectively constructed on the train set. On the basis of this, the combination of IMF models is determined by RMSE of the IMFs on validation set, and ultimately add them together to build a combination model. Finally, through the evaluation on the test set, the prediction accuracy of the combination model is verified. |
| Databáze: | OpenAIRE |
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