粒子群支持向量机在煤矿顶板故障诊断中的应用  

The Application of PSO Support Vector Machine in Fault Diagnosis of Coal Mine Roof

作　　者：张铁山[1] 付家才[1] 

机构地区：[1]黑龙江科技学院电气与信息工程学院,哈尔滨150027

出　　处：《机电一体化》2013年第7期84-87,共4页Mechatronics

基　　金：黑龙江省2012年研究生创新科研项目(YJSCX2012-324HLJ)

摘　　要：利用粒子群算法和遗传算法分别对支持向量机的惩罚因子C和核参数σ进行优化。仿真结果表明,粒子群算法具有全局寻优、运算量小和操作简便等特点,其搜索最优解的速度快于遗传算法的速度。将粒子群算法和支持向量机结合起来,提出了一种粒子群支持向量机分类器,并通过在煤矿顶板状态分类检测中的应用,验证了此粒子群支持向量机分类器具有高效的分类能力。In this paper, the penalty parameter C and kernel parameter σ of support vector machines is optimized using and genetic algorithm respectively. The simulation results show that the search speed of PSO is faster than genetic algorithms. And PSO has good global search, small calculated amount and simple operation. Then, the PSO support vector machine classifier is proposed. It is applied in the classification of coal mine roof failure. The results show that the PSO support vector machine classifier has efficient classification ability.

关 键 词：支持向量机 粒子群算法 煤矿顶板故障 遗传算法 分类器 

分 类 号：TH165.3[机械工程—机械制造及自动化] TD327.2[矿业工程—矿井建设]