基于LS-SVM的矿井巷道场强预测

Prediction of field intensity in mine tunnel based on LS-SVM

针对目前矿井巷道场强预测精度低的问题,提出采用最小二乘支持向量机方法建立预测模型,以某巷道实测数据作为训练样本,对矿井巷道场强进行预测;详细分析了训练集构造和参数选择对预测效果的影响。仿真结果表明,LS-SVM预测模型较双斜率模型和对数校正模型具有更高的预测精度。

For problem of low accuracy of current prediction of field intensity in mine tunnel,a prediction model based on the least squares support vector machine（LS-SVM）method was proposed to predict field intensity in mine tunnel by taking measured data of a tunnel as training sample.Influence of training set construction and parameters selection on prediction effect were analyzed in details.The simulation results show that the LS-SVM prediction model has higher prediction accuracy than dual-slope model and logarithmic correction model.