Skip Nav Destination
ASME Press Select Proceedings
International Conference on Information Technology and Computer Science, 3rd (ITCS 2011)
Editor
V. E. Muhin,
V. E. Muhin
National Technical University of Ukraine
Search for other works by this author on:
ISBN:
9780791859742
No. of Pages:
656
Publisher:
ASME Press
Publication date:
2011
eBook Chapter
52 Prediction of Coal Mine Gas Concentration Based on Constructive Neural Network
By
Yueqin Zhang
,
Yueqin Zhang
Taiyuan University of Technology
; 270668678@qq.com
Search for other works by this author on:
Qianqian Zeng
Qianqian Zeng
Taiyuan University of Technology
; 270668678@qq.com
Search for other works by this author on:
Page Count:
4
-
Published:2011
Citation
Zhang, Y, & Zeng, Q. "Prediction of Coal Mine Gas Concentration Based on Constructive Neural Network." International Conference on Information Technology and Computer Science, 3rd (ITCS 2011). Ed. Muhin, VE, & Hu, WB. ASME Press, 2011.
Download citation file:
This paper analyzes gas concentration data from different angles (levels) through a variety of size for the characteristic of gas concentration time series data set based on quotient space theory. Meanwhile, we deal with the information by quotient space and neural network model, and propose a constructive neural network model, which combines the covering algorithm and BP neural network model. Experimental results show that the model is better than the traditional method by the coal gas data obtained from some coal gas.
Abstract
Keywords
Introduction
Quotient Space Theory
Grey Correlation Theory
Constructive Neural Networks
Prediction of Gas Concentration Based on Constructive Neural Network
Conclusion
Acknowledgments
References
This content is only available via PDF.
You do not currently have access to this chapter.
Email alerts
Related Chapters
Modeling and Simulation of Coal Gas Concentration Prediction Based on the BP Neural Network
International Symposium on Information Engineering and Electronic Commerce, 3rd (IEEC 2011)
Estimating Resilient Modulus Using Neural Network Models
Intelligent Engineering Systems Through Artificial Neural Networks, Volume 17
Ensemble Neural Networks with Fuzzy Integration for Complex Time Series Prediction
Intelligent Engineering Systems through Artificial Neural Networks
Research and Application of Piecewise Linear Fitting Algorithm Based on Stock Time Series
International Conference on Computer Technology and Development, 3rd (ICCTD 2011)
Related Articles
The Reconstruction of Significant Wave Height Time Series by Using a Neural Network Approach
J. Offshore Mech. Arct. Eng (August,2004)
Application of Bayesian Forecasting to Change Detection and Prognosis of Gas Turbine Performance
J. Eng. Gas Turbines Power (March,2010)
Study on the Identification of Experimental Chaotic Vibration Signal for Nonlinear Vibration Isolation System
J. Comput. Nonlinear Dynam (October,2011)