Aiming at the non-stationary characteristics of gas outburst time series, a novel gas outburst
prediction model is presented in this paper. The proposed model is based on the extreme learning machine
and empirical mode decomposition. First, the gas concentration time series is decomposed into a
series of subsequence and residual quantity with EMD in order to reduce the calculation of local signal
analysis for gas concentration in the scale and improve the accuracy of prediction. Then, each of the
subsequence and residual quantity is predicted with ELM. Finally, the resultant prediction is obtained by combining the
molecular sequences and residual quantity prediction. Considering the acquisition of gas concentration at mine working
face as an example, the simulation results show that the EMD - ELM model is superior than ELM and LSSVM (Least
Squares Support Vector Machine) model in prediction accuracy and the training speed.
Keywords: Empirical mode decomposition. extreme learning machine, gas outburst, prediction.
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