摘要
以矿山台阶爆破块度分布分析为例,建立了爆破块度预测的SVM模型。预测模型与实测结果相比,平均相对误差为6.67%,与BP神经网络模型、R-R分布及G-G-S经验模型相比,SVM模型预测爆破块度具有明显的优越性和可靠性。
With example analysis of size distribution of rock fragment of bench blasting,a support vector machine（SVM） prediction model of rock fragment size for bench blasting was established.The predicted results obtained by the SVM prediction model were in accordance with measured results,the average relative error is 6.67%.Compare with BP neural network model,R-R distribution and G-G-S experience model,the SVM prediction model has obvious superiority and reliability in predicting the rock fragment size for blasting.
出处
《矿业研究与开发》
CAS
北大核心
2010年第5期97-99,共3页
Mining Research and Development
基金
国家自然科学基金项目(50764001)
国家“十一五”科技支撑计划项目(2007QAB00008)
贵州省科技攻关项目(黔科合GY字(2007)3015)
贵州省重大科技专项(1091115)
关键词
支持向量机
台阶爆破
爆破块度预测
Support vector machines
Bench blasting
Prediction of rock fragment size
