Abstract：In order to forecast the gas emission of mining working face, this paper utilized principal component analysis and genetic algorithm (GA) to optimize the coupling of the method of support vector machine (SVM), and took the advantages of absorbing the principal component analysis data dimension reduction in the sample data screening, As the result, the choice of data samples is concise and more representative. Making full use of support vector machine training speed can obtain the global optimal solution with characteristics of good performance of Shi, and by combining with genetic algorithm (GA), the optimal penalty parameter c and the kernel function parameter g are searched. The SVM prediction model of the mining working face gas emission was established based on PCA - GA, and was successfully applied in practice use. Research results show that the prediction model has the maximum relative error of 30.15%, the minimum relative error of 5.13%, and the average relative error of 12.8603%. Compared with other prediction model, this model has better generalization ability and higher prediction precision.
张强，贾宝山，董晓雷，等. PCA-GA-SVM的回采工作面瓦斯涌出量预测[J]. 辽宁工程技术大学学报(自然科学版), 2015, 34(5): 572-577.
ZHANG Qiang, JIA Baoshan, DONG Xiaolei. Working face gas emission prediction based on PCA-GA-SVM. Journal of LNTU.Natural Science, 2015, 34(5): 572-577.