基于粒子群支持向量机的矿岩强度指标的超声预测

建立以吸水率、干密度、波阻抗、动泊松比、动弹性模量为输入,抗压强度为输出的支持向量机预测模型.为了提高支持向量机预测精度,引入了粒子群算法对支持向量机的参数进行优化,克服支持向量机参数人工选取的不足.通过对鞍千矿和弓长岭矿的矿岩样本数据分析,该模型的预测误差最大为8.2%,精度明显高于传统神经网络法.结果表明基于超声波预测强度的方法具有很好效果,可望成为一种岩石强度预测的新方法.

Supersonic Forecast of the Strength of Ore and Rock Based on Particle Swarm Support Vector Machine

The forecast model based on support vector machine(SVM),which considers the water-absorbing capacity,dry density,wave impedance,Poisson ratio and elastic ratio as input index,and takes the compression strength as output index,was established.In order to increase the forecast accuracy of SVM,the particle swarm algorithm was used to optimize the SVM parameters,which can overcome the shortage for arbitrary selection of SVM parameters.Through analyzing the ore and rock sample data from Anqian mine and Gongchangling mine,the model obviously predicted more precise results than the nerve network method,with the biggest forecast error 8.2%.This result demonstrates that the method based on supersonic forecast strength is feasible and will be a new method for forecting rock strength.