Parameter optimization of support vector machine based on ant colony optimization algorithm

被引：0
|
作者：
Zhang, Bei-Lin [1 ,2 ]
Qian, Lin-Fang [1 ]
Cao, Jian-Jun [2 ]
Ren, Guo-Quan [2 ]
机构：
[1] School of Mechanical Engineering, NUST, Nanjing 210094, China
[2] Department of Artillery Engineering of Ordnance Engineering College, Shijiazhuang 050003, China
来源：
Nanjing Li Gong Daxue Xuebao/Journal of Nanjing University of Science and Technology | 2009年 / 33卷 / 04期
关键词：
Fault detection - Ant colony optimization - Failure analysis - Vectors - Parameter estimation - Radial basis function networks;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
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.
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页码：464 / 468
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