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高斯径向基核函数参数的GA优化方法 被引量：9

GA based parameter optimization of Gauss kernel function
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摘要 核Fisher判别法KFDA(Kernel Fisher Discriminant Analysis)在模式分类应用中通常采用高斯径向基函数做核函数,但高斯径向基函数中参数σ的选取对模式分类的效果影响较大。参数σ的选取目前仅凭经验,缺乏自动选取方法。提出采用遗传算法GA(Genetic Algorithm)实现自动优化参数σ使KFDA具有自适应性的方法,用GA优化参数σ所确定的高斯径向基核函数应用于KFDA时,模式分类的可分性测度大。该方法在电机滚动轴承故障分类实验表明优于其他KFDA分类效果。 KFDA（Kemel Fisher Discriminant Analysis） usually adopts the Gauss radial basis function as its kernel function for mode classification ,in which the selection of parameter influences the result greatly. The value of the parameter is normally determined by experience. It is proposed to use GA in the automatic optimization of the parameter to improve the adaptability of KFDA,which makes the classification resolution higher. Its application in fault classification of motor rolling bearing shows its superiority.
作者 徐咏梅 柳桂国 柳贺
机构地区 浙江工商职业技术学院 华东理工大学工业自动化国家工程中心分部
出处 《电力自动化设备》 EI CSCD 北大核心 2008年第6期52-55,共4页 Electric Power Automation Equipment
关键词 核Fisher判别法 遗传算法 核函数 滚动轴承 故障分类 KFDA GA kernel function rolling bearing fault classification
分类号 TP301 [自动化与计算机技术—计算机系统结构]
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参考文献11

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电力自动化设备
2008年 第6期

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