【摘要】 针对相关向量机(RVM)计算复杂度大、训练时间长的问题,提出一种基于快速相关向量机(FRVM)的优化算法,其大大减少了相关向量机的训练时间,提高了分类的精度。将它应用于电能质量扰动分类中,首先对电能质量扰动信号进行基于小波变换的时频分析,提取小波变换各层信号的能量与标准信号的能量之差组成特征向量;然后用FRVM对特征量进行分类,进而实现基于小波变换和FRVM的电能质量扰动分类新方法。实验仿真验证了该方法能够对各类电能质量扰动信号进行分类,并且其分类效率和准确率均优于传统的相关向量机分类方法。更多还原

【Abstract】 To reduce the computational complexity and long training time in relevance vector machine(RVM),this paper proposed an optimized algorithm based on fast relevance vector machine(FRVM),which not only greatly reduces the training time of relevance vector machine,but also improves its classification accuracy.This method is applied to the classification of power quality disturbances.Firstly,the wavelet transform is applied to analysis the time-frequency features of the power quality disturbances,and the difference of the energy of the wavelet transform signal in each layer and the standard signal energy is used as feature vector.Secondly,FRVM is used to classify the feature vector to realize power quality disturbances classification based on wavelet transform and FRVM.The simulation verifies that this method can classify all kinds of power quality disturbances,and has higher classification efficiency and accuracy than the classical RVM.更多还原