[1]张培林,等.基于蚁群算法的支持向量机参数优化[J].南京理工大学学报(自然科学版),2009,(04):464-468.
 ZHANG Bei-lin,QIAN Lin-fang,CAO Jian-jun,et al.Parameter Optimization of Support Vector Machine Based on Ant Colony Optimization Algorithm[J].Journal of Nanjing University of Science and Technology,2009,(04):464-468.
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基于蚁群算法的支持向量机参数优化
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《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

卷:
期数:
2009年04期
页码:
464-468
栏目:
出版日期:
2009-08-30

文章信息/Info

Title:
Parameter Optimization of Support Vector Machine Based on Ant Colony Optimization Algorithm
作者:
张培林 1 2 钱林方 1 曹建军 2 任国全 2
1. 南京理工大学机械工程学院,江苏南京210094; 2. 军械工程学院火炮工程系, 河北石家庄050003
Author(s):
ZHANG Bei-lin12QIAN Lin-fang1CAO Jian-jun2REN Guo-quan2
1.School of Mechanical Engineering,NUST,Nanjing 210094,China;2.Department of Artillery Engineering of Ordnance Engineering College,Shijiazhuang 050003,China
关键词:
蚁群算法 支持向量机 参数优化 油液分析 故障诊断
Keywords:
ant colony optimization algorithm support vector machine parameter optimization oil analysis fault diagnosis
分类号:
TP18
摘要:
针对支持向量机的参数对分类性能的影响,探讨了基于蚁群算法的支持向量机参数优化方法,建立了支持向量机参数优化模型,给出了基于网格划分策略的连续蚁群算法,并将其用于优化模型求解,通过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。通过仿真和应用实例,验证了方法的有效性,得到了95%以上的分类正确率。
Abstract:
Parameters of support vector machine is the key factor that impacts its classifying performance.A parameter optimization method for support vector machine using ant colony optimization algorithm is discussed.A parameter optimization model is established.The continuous ant colony optimization method based on gridding partition is given and used to resolve the optimization model.The classifying performance reaches the best state by optimizing the penalty factor and the radial basis function.The validity of the method is tested by simulation and application instances,and more than 95% classified right rate is obtained.

参考文献/References:

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备注/Memo

备注/Memo:
基金项目: 国家自然科学基金( 50705097)
作者简介: 张培林( 1955- ), 男,教授, 博士生,主要研究方向: 机械工程及自动化。
更新日期/Last Update: 2012-11-19