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Forecasting Model of Coal Mine Water Inrush Based on Extreme Learning Machine |
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PP: 1243-1250 |
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Author(s) |
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Zuopeng Zhao,
Pei Li,
Xinzheng Xu,
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Abstract |
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| In order to satisfy the real-time requirement of the coal mine water inrush, comprehensively considering the master
influencing factors filtered out by using principal component analysis (PCA) of coal mine water inrush, a forecasting model of coal
mine water inrush based on extreme learning machine (ELM) is proposed in this paper by combining with the characteristics of single
hidden layer feedforward networks (SLFNs). The model is used to test the samples, and then compare the experimental results of ELM
with back-propagation (BP) and support vector machine (SVM). The experimental results show that, compared with BP and SVM, this
method is not only learns fast but also has good generalization performance , and thus it can satisfy the real-time requirements of coal
mine water inrush effectively. The feasibility of ELM for coal mine water inrush forecast and the availability of the algorithm were
validated through experiments. |
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