DocumentCode :
2998736
Title :
A novel approach to coal and gas outburst prediction based on multi-sensor information fusion
Author :
Ma, Xiaoping ; Miao, Yanzi ; Zhao, Zhongxiang ; Zhang, Houxiang ; Zhang, Jianwei
Author_Institution :
Sch. of Inf. & Electr. Eng., China Univ. of Min. & Technol., Xuzhou
fYear :
2008
fDate :
1-3 Sept. 2008
Firstpage :
1613
Lastpage :
1618
Abstract :
Based on introduction of the background and the limitations of present prediction methods for gas outburst in coal mines, this paper focuses on introducing a new decision-making approach to coal and gas outburst prediction with multi-sensor information fusion. Two of the multi-sensor information fusion methods, neural network and the Dempster-Shafter evidence theory, were taken into account, and the improved combination rules of the D-S evidence theory in fuzzy sets was given for decision fusion. Then the practical experiment of gas outburst prediction is given to prove the efficiency and effectiveness of the new approach. The related experiments show that the novel approach with improved combination rules of the D-S evidence theory provides more rational results than each single prediction method.
Keywords :
coal; decision making; fuzzy set theory; gas industry; mining industry; neural nets; sensor fusion; D-S evidence theory; Dempster-Shafter evidence theory; coal mines; coal outburst prediction; decision fusion; decision-making approach; fuzzy sets; gas outburst prediction; multisensor information fusion; neural network; Automation; Decision making; Fuzzy neural networks; Fuzzy sets; Informatics; Logistics; Neural networks; Noise measurement; Prediction methods; Sensor phenomena and characterization; Coal and Gas Outburst Prediction; Fuzzy combination rules; information fusion; multi-sensor; neural networks; the D-S evidence theory;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Automation and Logistics, 2008. ICAL 2008. IEEE International Conference on
Conference_Location :
Qingdao
Print_ISBN :
978-1-4244-2502-0
Electronic_ISBN :
978-1-4244-2503-7
Type :
conf
DOI :
10.1109/ICAL.2008.4636412
Filename :
4636412
Link To Document :
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