基於GA-LSSVR算法的回采工作麵瓦斯湧出量預測

摘　要:針對回采工作麵瓦斯湧出量問題的小樣本、非線性、影響因素關係複雜等特點,采用遺傳-最小二乘支持向量回歸算法對瓦斯湧出量進行預測,利用定量方法進行分析,避免了定性分析的局限性,有效提高了預測的精度.該模型首先利用遺傳算法對最小二乘支持向量回歸機中的參數進行訓練和優化,然後運用遺傳-最小二乘支持向量回歸模型對測試樣本進行了回采工作麵瓦斯湧出量測試.測試結果表明：與支持向量回歸機以及最小二乘支持向量回歸機的預測值相比,遺傳-最小二乘支持向量回歸的回采工作麵瓦斯湧出量預測可靠性和精確性更高.

關鍵詞:瓦斯湧出量 回采工作麵 預測 最小二乘支持向量回歸機 遺傳算法

分類號:　TD712.5Prediction of gas emission quantity of the working face based on genetic algorithm-least squares support vector regressionCAO Qing - kui, SHANG Na - xin (School of Economics and Management, Hebei University of Engineering, Hebei Handan 056038, China)Abstract:The problem of the gas emission of the working face was characterized by small samples, nonlinear, and it was affected by complex factors. Using the genetic - least squares support vector re- gression algorithm to predict the gas emission could avoid the qualitative analysis limitations and effec- tively improve the accuracy of the forecast, because it was a quantitative method for analysis. First, the model used the genetic algorithms to train and optimize the least squares support vector regression parameters, and then used the genetic - least squares support vector regression model to predict the a- mount of gas emission of test samples. The test results show that ： the genetic algorithm - least squares support vector regression model has a higher reliability and accuracy, compared to the predicted val- ues of the support vector regression and the least squares support vector regression.

Keyword:gas emission quantity ; working face ; prediction ; least squares support vector regression ;genetic algorithm