支持向量机法在煤与瓦斯突出分析中的应用研究

通过分析采煤工作面煤与瓦斯涌出量与地质构造指标的对应关系,应用支持向量机(SVM)方法对煤与瓦斯涌出类型及涌出量进行分析。建立两类突出识别的SVM模型、多类型突出识别的H-SVMs模型以及预测瓦斯涌出量的支持向量回归模型。研究结果表明:SVM方法能够很好地对煤与瓦斯突出模式进行识别,所建立的采煤工作面瓦斯涌出量预测模型的精度高于应用BP神经网络预测精度;SVM理论基础严谨,决策函数结构简单,泛化能力强,并且决策函数中的法向量W可以反映突出模式识别的地质结构指标的权重。

Application Study of SVM in Analysis of Coal and Gas Outburst

The correspondence of the amount of gas outburst in working face in mine to the geological indexes was researched, and the categories and amount of coal and gas outburst were also analyzed with support vector machine (SVM) method. Then, the SVM model for recognizing two categories of coal and gas outburst, the H-SVMs model for recognizing multi-category of coal and gas outburst, and the SVM regression model for predicting the amount of coal and gas outburst were constructed. The experimental results show that SVM is a good method to recognize the styles of coal-gas outburst;and the SVM regression model is better than the BP model at predicting the amount of coal-gas outburst, because SVM is based on a strict mathematical theory, has a simple structure and a good generalization performance, and can reflect the weights of geological indexes in outburst style recognition through the parameter W in the decision function.