摘要
有机地结合贝叶斯分类法、感知准则函数分类法和最小错分样本数准则分类法的理论优点,提出了一种新的组合分类方法。该方法保留贝叶斯法的最优特点,去除其使用的必备条件,保留了最小错分样本数准则分类法可对交叉样本分类,去除其计算复杂的缺点。与常用的最小错分样本数准则函数分类法以及贝叶斯分类法、感知准则函数分类法相比,该组合分类法在理论上具有最优分类、可对交叉样本分类、计算简便的优点。在对暂态电能质量扰动分类的仿真分析过程中,充分显示了新方法的上述优点。
A new classification method is put forward in this paper which combines the advantages of the Bayes method and the rule of perception function and the rule of least wrong. This new method keeps the advantages of the traditional methods of classification but removes their shortcomings. Comparing to the classification methods commonly uesd, the new method has several advantages including optimal classification and simple computation and classifying the cross samples. When classifying the transient power quality disturbances, the new method shows all these advantages.
出处
《电力系统保护与控制》
EI
CSCD
北大核心
2010年第5期1-4,共4页
Power System Protection and Control
关键词
分类
交叉样本
电能质量
暂态扰动
classification
cross sample
power quality
transient power quality disturbance