基于GA-LSSVR算法的回采工作面瓦斯涌出量预测

针对回采工作面瓦斯涌出量问题的小样本、非线性、影响因素关系复杂等特点,采用遗传-最小二乘支持向量回归算法对瓦斯涌出量进行预测,利用定量方法进行分析,避免了定性分析的局限性,有效提高了预测的精度。该模型首先利用遗传算法对最小二乘支持向量回归机中的参数进行训练和优化,然后运用遗传-最小二乘支持向量回归模型对测试样本进行了回采工作面瓦斯涌出量测试。测试结果表明:与支持向量回归机以及最小二乘支持向量回归机的预测值相比,遗传-最小二乘支持向量回归的回采工作面瓦斯涌出量预测可靠性和精确性更高。

Prediction of gas emission quantity of the working face based on genetic algorithm-least squares support vector regression

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 regression algorithm to predict the gas emission could avoid the qualitative analysis limitations and effectively 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 amount 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 values of the support vector regression and the least squares support vector regression.