DocumentCode
2601291
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
fYear
2011
fDate
26-29 June 2011
Firstpage
151
Lastpage
154
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;
fLanguage
English
Publisher
ieee
Conference_Titel
Modelling, Identification and Control (ICMIC), Proceedings of 2011 International Conference on
Conference_Location
Shanghai
Type
conf
DOI
10.1109/ICMIC.2011.5973692
Filename
5973692
Link To Document