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Received:June 15, 2014 Revised:July 17, 2014
Received:June 15, 2014 Revised:July 17, 2014
中文摘要: 瓦斯涌出量的预测在煤矿安全问题中具有重要意义, 利用小波分析将原始数据进行分解并重构, 并利用AFT加快计算速度, 重构后得到的数据通过灰色模型进行预测, 将利用小波-灰色模型预测后的结果与单独用灰色模型预测的结果进行比较, 结果表明, 前者的预测精度明显高于后者.
Abstract:The predict of the gas emission is of great significance in the coal mine safety, decompose and refactoring the original data by wavelet analysis, and speed up by AFT, the data refactored predict by GM(1,1). We compare the results between wavelet - grey and GM(1,1). The results show that the prediction accuracy of the former is higher than the latter.
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郭长娜,王洋洋,吴北平.基于小波分析的煤矿瓦斯涌出量灰色预测模型.计算机系统应用,2015,24(3):147-150
GUO Chang-Na,WANG Yang-Yang,WU Bei-Ping.Grey Prediction Model of the Gas Emission Based on Wavelet Analysis.COMPUTER SYSTEMS APPLICATIONS,2015,24(3):147-150
郭长娜,王洋洋,吴北平.基于小波分析的煤矿瓦斯涌出量灰色预测模型.计算机系统应用,2015,24(3):147-150
GUO Chang-Na,WANG Yang-Yang,WU Bei-Ping.Grey Prediction Model of the Gas Emission Based on Wavelet Analysis.COMPUTER SYSTEMS APPLICATIONS,2015,24(3):147-150
