A prediction method for gas emission based on RBF with grey correlation analysis

A rolling method of gas emission based on RBF neural networks is improved. In this method, a part of fixed-length data is selected for the prediction, new data are added continuously to the input sequence, and old data are removed, thereby developing the rolling prediction model. The diversified fac...

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Bibliographic Details
Main Authors: Yumin Pan, Hongmei Ma, Quanzhu Zhang, Pengqian Xue
Format: Conference Proceeding
Language:English
Subjects:
Accuracy
Artificial neural networks
Coal
Data models
Predictive models
Training
Online Access:Request full text
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Summary:A rolling method of gas emission based on RBF neural networks is improved. In this method, a part of fixed-length data is selected for the prediction, new data are added continuously to the input sequence, and old data are removed, thereby developing the rolling prediction model. The diversified factors of gas emission analyzed have grey correlation. As a result, the model designed using this method can generalize well. The simulation results also show that the improved rolling prediction model applied in gas emission prediction has reliable accuracy and a good convergence rate.
DOI:10.1109/ICMIC.2011.5973692