Title :
A prediction method for gas emission based on RBF with grey correlation analysis
Author :
Pan, Yumin ; Ma, Hongmei ; Zhang, Quanzhu ; Xue, Pengqian
Author_Institution :
Dept. of Electron. Inf. Eng., North China Inst. of Sci. & Technol., Beijing, China
Abstract :
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.
Keywords :
grey systems; natural gas technology; prediction theory; radial basis function networks; RBF neural network; diversified factor; fixed-length data; gas emission prediction; grey correlation analysis; reliable accuracy; rolling method; rolling prediction model; Accuracy; Artificial neural networks; Coal; Data models; Predictive models; Training;
Conference_Titel :
Modelling, Identification and Control (ICMIC), Proceedings of 2011 International Conference on
Conference_Location :
Shanghai
DOI :
10.1109/ICMIC.2011.5973692
