DocumentCode
2650333
Title
Notice of Retraction
Gas bearing capacity forecasting method based on ant colony optimization and support vector regression
Author
Chunliu Sun ; Hanmin Xiao ; Weidong Liu ; Linghui Sun
Author_Institution
Inst. of Porous Flow & Fluid Mech. CNPC, Chinese Acad. of Sci., Langfang, China
Volume
7
fYear
2010
fDate
16-18 April 2010
Abstract
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
In recent years, new energy´s exploitation is becoming more and more important, one type of the new energy is gas bearing in the coal layers. Forecasting for coal-seam gas content is a complicated non-linear forecasting problem, which is difficult to solve. A prediction model of coal-seam gas capacity based on support vector regression and ant colony optimization is presented in this paper. As there is the relationship between the operational parameters of support vector regression and support vector regression prediction model, ant colony optimization is applied to select the operational parameters of support vector regression. The experimental results indicate that the forecasting precision of support vector regression and ant colony optimization is better than that of BP neural network. It is indicated that the prediction model meets the requirement of coal-seam gas content prediction.
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
In recent years, new energy´s exploitation is becoming more and more important, one type of the new energy is gas bearing in the coal layers. Forecasting for coal-seam gas content is a complicated non-linear forecasting problem, which is difficult to solve. A prediction model of coal-seam gas capacity based on support vector regression and ant colony optimization is presented in this paper. As there is the relationship between the operational parameters of support vector regression and support vector regression prediction model, ant colony optimization is applied to select the operational parameters of support vector regression. The experimental results indicate that the forecasting precision of support vector regression and ant colony optimization is better than that of BP neural network. It is indicated that the prediction model meets the requirement of coal-seam gas content prediction.
Keywords
backpropagation; coal; forecasting theory; gas industry; neural nets; optimisation; regression analysis; support vector machines; BP neural network; ant colony optimization; coal layers; coal-seam gas capacity; coal-seam gas content prediction; energy exploitation; forecasting precision; gas bearing capacity forecasting method; nonlinear forecasting problem; operational parameters; support vector regression prediction model; Ant colony optimization; Artificial intelligence; Artificial neural networks; Load forecasting; Neural networks; Petroleum; Predictive models; Safety; Sun; Training data; ant colony optimization; coal-seam gas content; forecasting; operational parameters;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Engineering and Technology (ICCET), 2010 2nd International Conference on
Conference_Location
Chengdu
Print_ISBN
978-1-4244-6347-3
Type
conf
DOI
10.1109/ICCET.2010.5485487
Filename
5485487
Link To Document