Abstract
Gas concentration is one of key factors which influence safe production of coal mine, and it is very important to forecast gas concentration accurately for ensuring the coal mine safety. A novel approach is presented to forecast gas concentration based on support vector regression (SVR) in correlation space reconstructed by kernel principal component analysis (KPCA). A two-stage architecture is proposed to improve its prediction accuracy and generalization performance for gas concentration forecasting. In the first stage, KPCA is adopted to extract features and obtain kernel principal components, so the correlation space of gas concentration is reconstructed according to the accumulative contribution ratio. Then, in the second stage, support vector regression (SVR) is employed for forecasting gas concentration, which hyperparameters are selected by adaptive chaotic cultural algorithm (ACCA). The approach is compared with the forecasting model in whole space of gas concentration. The simulation shows that SVR model in correlation space using KPCA performs much better than that without correlation analysis.
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Cheng, J., Qian, Js., Niu, Gd., Guo, Yn. (2009). Gas Concentration Forecasting Based on Support Vector Regression in Correlation Space via KPCA. In: Yu, W., He, H., Zhang, N. (eds) Advances in Neural Networks – ISNN 2009. ISNN 2009. Lecture Notes in Computer Science, vol 5551. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01507-6_106
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DOI: https://doi.org/10.1007/978-3-642-01507-6_106
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01506-9
Online ISBN: 978-3-642-01507-6
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