DocumentCode :
2621442
Title :
The application of fuzzy clustering and algebra neural network in coal combustion forecasting and warning
Author :
Long, Xihua ; Li, Baolin ; Yang, Xinjia
Author_Institution :
Comput. Sci. & IT, Xi´´an Univ. of Sci. & Technol., Xi´´an, China
fYear :
2011
fDate :
27-29 June 2011
Firstpage :
799
Lastpage :
802
Abstract :
For the forecasting and waring system of coal spontaneous combustion, the using of multi-index data fusion technology for predicting the dangerous degree of coal spontaneous combustion is more scientific and reliability than the single index parameter.Through extracting multi-index gases and gas-ratio based on sensitivity and regularity of index gases ,such as N2, O2, CO, CO2, CH4, C2H2 and so on, and according the nonlinear mapping relationship between temperature and gas index, the applications of neural network and cluster analysis in the previous forecast and real-time monitoring provide reliable guarantee for coal mine safety, and ensure the establishing of automatic alarming software system.
Keywords :
alarm systems; algebra; coal; combustion; forecasting theory; mining; neural nets; pattern clustering; sensor fusion; statistical analysis; algebra neural network; automatic alarming software system; cluster analysis; coal combustion forecasting; coal combustion warning; coal mine safety; coal spontaneous combustion; fuzzy clustering; gas index; multiindex data fusion technology; multiindex gas extraction; nonlinear mapping relationship; real-time monitoring; single index parameter; Artificial neural networks; Coal; Combustion; Forecasting; Pattern recognition; Temperature distribution; Algebra Neural Network; Data Fusion; Forecasting and Warning software; Fuzzy Clustering; Pattern recognition; coal spontaneous combustion;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Science and Service System (CSSS), 2011 International Conference on
Conference_Location :
Nanjing
Print_ISBN :
978-1-4244-9762-1
Type :
conf
DOI :
10.1109/CSSS.2011.5974733
Filename :
5974733
Link To Document :
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