摘要
受限于空区形成条件及其变化的复杂性、随机性,传统用于空区顶板围岩冒落高度及碴石厚度预测的方法往往预测精度偏低。为此,在结合各种单一人工智能预测方法优势的基础上,提出了一种新的预测模型——残差型灰色最小二乘支持向量机预测模型(G-LSSVM),将该模型应用到某铁矿的覆盖层冒落高度及碴石厚度预测中,预测的结果与实际钻孔摄影监测相比,结果极为接近。研究表明:残差型G-LSSVM用于覆盖层冒落高度及碴石厚度预测是有效可行的。
Due to the complexity and randomness of the forming condition of the mined-out area and its change,the prediction accuracy for roof rock caving height and ballast stone thickness of the mined-out area by the traditional method is often lower.In this paper,combining with the advantages of various single forecasting methods based on artificial intelligence,a forecasting model of residual grey squares new support vector machine prediction model（G-LSSVM） is put forward and then applied to predicting the roof caving height and ballast stone thickness of an iron ore.The predicting results are very close to the actual drilling photography monitoring result.The tests show： the residual type G-LSSVM for predicting the covering caving height and ballast stone thickness is feasible and effective.
出处
《金属矿山》
CAS
北大核心
2013年第8期8-10,15,共4页
Metal Mine
关键词
残差型
最小二乘支持向量机
覆盖层冒落高度
碴石厚度
预测
钻孔摄影
Residual type
Least squares support vector machine
Covering caving height
Ballast stone thickness
Prediction
Drilling photography
