A wavelet-based robust relevance vector machine based on sensor data scheduling control for modeling mine gas gushing forecasting on virtual environment

Mathematical Problems in Engineering

Volumn 2013, Issue , 2013, Pages

  Ting, Wang ^a   Lin Qin, Cai ^a   Yao, Fu ^b   Tingcheng, Zhu ^b  

^a CHONGQING UNIVERSITY OF POSTS AND TELECOMMUNICATIONS   (China)

^b NORTHEAST NORMAL UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

KERNEL FUNCTION; MEAN PERCENTAGE; MORLET WAVELET FUNCTION; PREDICTION ACCURACY; PREDICTION ERRORS; RELEVANCE VECTOR MACHINE; RVM MODELS; SENSOR DATA;

GASES; SCHEDULING; SENSORS; VIRTUAL REALITY;

FORECASTING;

EID: 84880152914     PISSN: 1024123X     EISSN: 15635147     Source Type: Journal    
DOI: 10.1155/2013/579693     Document Type: Article

References (16)

1. Zhang J., The application of analytic hierarchy process in mine gas prevention system. Procedia Engineering 2011 1576 1584 2-s2.0-84856098989 10.1016/j.proeng.2011.11.2341
2. Packham R., Cinar Y., Moreby R., Simulation of an enhanced gas recovery field trial for coal mine gas management. International Journal of Coal Geology 2011 85 3-4 247 256 2-s2.0-79951553140 10.1016/j.coal.2010.11.013
3. Yang W., Lin B., Zhai C., Li X., An S., How in situ stresses and the driving cycle footage affect the gas outburst risk of driving coal mine roadway. Tunnelling and Underground Space Technology 2012 31 139 148 10.1016/j.tust.2012.04.015
4. Wang K. X., Fu X. H., Zhou Y. A., HE Y., Wu H., Dynamic development characteristics of amounts of gas and levels of pressure in the Pan-1 coal mine of Huainan. Mining Science and Technology 2009 19 6 740 744 2-s2.0-70449729805 10.1016/S1674-5264(09)60135-6
5. Wang W., Yan J., The geological factor analysis of influenced Tianchi coal mine gas occurrence. Procedia Engineering 2012 45 317 321 10.1016/j.proeng.2012.08.164
6. Wang L., Cheng Y. P., Wang L., Guo P. K., Li W., Safety line method for the prediction of deep coal-seam gas pressure and its application in coal mines. Safety Science 2012 50 3 523 529 2-s2.0-84855356839 10.1016/j.ssci.2011.09.022
7. Lamrini B., Della Valle G., Trelea I. C., Perrot N., Trystram G., A new method for dynamic modelling of bread dough kneading based on artificial neural network. Food Control 2012 26 2 512 524 2-s2.0-84857717489 10.1016/j.foodcont. 2012.01.011
8. Lafdani E. K., Nia A. M., Ahmadi A., Daily suspended sediment load prediction using artificial neural networks and support vector machines. Journal of Hydrology 2013 478 50 62
9. Khashei M., Bijari M., A novel hybridization of artificial neural networks and ARIMA models for time series forecasting. Applied Soft Computing Journal 2011 11 2 2664 2675 2-s2.0-78751608738 10.1016/j.asoc.2010.10.015
10. Chuang C. C., Lee Z. J., Hybrid robust support vector machines for regression with outliers. Applied Soft Computing Journal 2011 11 1 64 72 2-s2.0-77957891947 10.1016/j.asoc.2009.10.017
11. Park J., Kwon I. H., Kim S. S., Baek J. G., Spline regression based feature extraction for semiconductor process fault detection using support vector machine. Expert Systems with Applications 2011 38 5 5711 5718 2-s2.0-79151477537 10.1016/j.eswa.2010.10.062
12. Ensafi A. A., Hasanpour F., Khayamian T., Mokhtari A., Taei M., Simultaneous chemiluminescence determination of thebaine and noscapine using support vector machine regression. Spectrochimica Acta A 2010 75 2 867 871 2-s2.0-73749084376 10.1016/j.saa.2009.12.021
13. Apolloni B., Malchiodi D., Valerio L., Relevance regression learning with support vector machines. Nonlinear Analysis, Theory, Methods and Applications 2010 73 9 2855 2864 2-s2.0-77955660099 10.1016/j.na.2010.06.035 ZBL1205.68279
14. Fokoué E., Sun D., Goel P., Fully Bayesian analysis of the relevance vector machine with an extended hierarchical prior structure. Statistical Methodology 2011 8 1 83 96 2-s2.0-78649707510 10.1016/j.stamet.2010. 05.005 ZBL1213.62043
15. Liu W., Tang B., A hybrid time-frequency method based on improved Morlet wavelet and auto terms window. Expert Systems with Applications 2011 38 6 7575 7581 2-s2.0-79951581566 10.1016/j.eswa.2010.12.107
16. Tang B., Liu W., Song T., Wind turbine fault diagnosis based on Morlet wavelet transformation and Wigner-Ville distribution. Renewable Energy 2010 35 12 2862 2866 2-s2.0-77954313196 10.1016/j.renene.2010.05.012