NDLI: Coalmine Gas Concentration Forecasting Based on Chaotic Theory and Neural Network Model

Content Provider	IEEE Xplore Digital Library
Author	Jin-Xian Zhao Hong-Zhang Jin Guang-Hua Yu
Copyright Year	2010
Abstract	Coalmine gas explosion is unique to the extremely serious type of disaster. The root cause of gas explosion accident is the Overrun of the gas concentration. Gas concentration is forecast to achieve effective prevention of gas explosion accidents. According to the non-linear of gas concentration and the predictability of the chaotic time series, gas concentration phase space was reconstructed by the Takens theory. In the first, the time delay was attained by the mutual information method. Secondly the embedding dimension was determined by GP algorithm and the chaotic time series was predicted by the BP neural network. Finally, an example is given which shows the forecast results could approximate the actual situation well, and accomplishing the forecast objection of gas concentration.
Starting Page	35
Ending Page	38
File Size	398795
Page Count	4
File Format	PDF
ISBN	9781424470815
e-ISBN	9781424470822
DOI	10.1109/ICIC.2010.15
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-06-04
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Chaos phase space reconstruction Delay effects Artificial neural networks Predictive models gas concentration Educational institutions Explosions State-space methods neural network chaotic time series Neural networks Computer networks Accidents
Content Type	Text
Resource Type	Article

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