Abstract
Because coal and gas outburst prediction are very complex. In recent years, using least square support vector machine (LS-SVM) time series forecasting model to predict mine working gas is proposed. However in the search support vector solution process, inequality constraints become equality constraints in the LS-SVM, its advantage is to improve the algorithm speed, at the same time the sparse of support vectors and robustness to model are loss. In this paper, weighted LS-SVM is proposed to improve sparse and robustness and its time series prediction model is used to analysis short-time mine working face gas emission. Under MATLAB2009b environment, using LS-SVM1.7 toolbox, specific algorithm model is established, further model is verified by Hebi 10th 1113 mine and gas outburst working face time series data. The results showed that: weighted LS-SVM can achieve a better short-time gas prediction than standard LS-SVM; meanwhile its model has a better robustness.
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Qiao, T., Qiao, M. (2011). Mine Working Face Gas Prediction Based on Weighted LS-SVM. In: Deng, H., Miao, D., Lei, J., Wang, F.L. (eds) Artificial Intelligence and Computational Intelligence. AICI 2011. Lecture Notes in Computer Science(), vol 7004. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23896-3_79
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DOI: https://doi.org/10.1007/978-3-642-23896-3_79
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