| 基于粒子群优化的支持向量机在机械模式分类中的应用 |
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| 引用本文： | 刘占军,康建设,张星辉.基于粒子群优化的支持向量机在机械模式分类中的应用[J].微计算机应用,2010,31(12). |
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| 作者姓名： | 刘占军 康建设 张星辉 |
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| 作者单位： | 军械工程学院装备指挥与管理系; |
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| 摘 要： | 机械故障诊断本质上是一个模式分类问题.支持向量机由于解决分类问题有着较好的表现,得到了日益广泛的应用.针对支持向量机的参数对分类性能的影响,采用粒子群算法对支持向量机的惩罚因子和径向基核函数进行优化,使支持向量机的分类性能最优,并将其应用于实例,得到了较好的分类正确率.
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| 关 键 词： | 支持向量机 粒子群算法 模式分类 |
Application of Machine Pattern Classification Based on Support Vector Machine with Particle Swarm Optimization |
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| LIU Zhanjun,KANG Jianshe,ZHANG Xinghui.Application of Machine Pattern Classification Based on Support Vector Machine with Particle Swarm Optimization[J].Microcomputer Applications,2010,31(12). |
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| Authors: | LIU Zhanjun KANG Jianshe ZHANG Xinghui |
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| Affiliation: | LIU Zhanjun,KANG Jianshe,ZHANG Xinghui(Department of Equipment Command and Management,Ordnance Engineering College,ShiJiazhuang,050003,China) |
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| Abstract: | Machine fault diagnosis is a problem of pattern classification Because of the excellent performance on classification,SVM(Support Vector Machine) is more and more widely used to solve classified problems now PSO(Particle Swarm Optimization) is used to optimize the penalty factor and the radial basis function based on their impact of classifying performance of SVM In this way,the classifying performance reaches the best state Further more,classified right rate is obtained by application instances |
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| Keywords: | Support vector machine Particle swarm optimization Pattern classification |
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