东北大学学报（自然科学版）

东北大学学报(自然科学版) ›› 2012, Vol. 33 ›› Issue (5): 731-734.DOI: -

• 论著 • 上一篇    下一篇

基于粒子群支持向量机的矿岩强度指标的超声预测

邱景平;邢军;姜谙男;孙晓刚;   

  1. 东北大学资源与土木工程学院;大连海事大学交通与物流工程学院;
  • 收稿日期:2013-06-19 修回日期:2013-06-19 发布日期:2013-04-04
  • 通讯作者: -
  • 作者简介:-
  • 基金资助:
    国家“十二五”科技攻关项目(2011BAB07B02,2011BAJ17B01)

Supersonic forecast of the strength of ore and rock based on particle swarm support vector machine

Qiu, Jing-Ping (1); Xing, Jun (1); Jiang, An-Nan (2); Sun, Xiao-Gang (1)   

  1. (1) School of Resources and Civil Engineering, Northeastern University, Shenyang 110819, China; (2) School of Traffic and Logistics Engineering, Dalian Maritime University, Dalian 116026, China
  • Received:2013-06-19 Revised:2013-06-19 Published:2013-04-04
  • Contact: Qiu, J.-P.
  • About author:-
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摘要： 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.

关键词: 矿岩强度, 超声波检测, 强度峰前预测, 支持向量机, 粒子群算法

Abstract: 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.

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