CAJ | 학술논문

研究电路的故障问题,应提高快速性和准确性.为提高仿真电路故障诊断效率,给出了一种基于改进支持向量机的仿真电路故障诊断方法.首先通过小波包变换实现了信号的能量特征提取,根据主元分析完成了特征压缩;其次针对支持向量机多分类一对一方法存在的不可分类区,将其与最近邻分类法相结合,实现了电路的故障诊断,并提出了一种混合遗传算法实现了小波函数和支持向量机参数的同步选择;最后通过一仿真电路的仿真实验,与BP,RBF和PNN等神经网络对比,结果显示基于支持向量机的方法诊断精度最高,达到98%,为设计提供参考依据.
연구전로적고장문제,응제고쾌속성화준학성.위제고방진전로고장진단효솔,급출료일충기우개진지지향량궤적방진전로고장진단방법.수선통과소파포변환실현료신호적능량특정제취,근거주원분석완성료특정압축;기차침대지지향량궤다분류일대일방법존재적불가분류구,장기여최근린분류법상결합,실현료전로적고장진단,병제출료일충혼합유전산법실현료소파함수화지지향량궤삼수적동보선택;최후통과일방진전로적방진실험,여BP,RBF화PNN등신경망락대비,결과현시기우지지향량궤적방법진단정도최고,체도98%,위설계제공삼고의거.
To improve the efficiency of analog circuit fault diagnosis, a method based on improved SVM is pro-posed. At first, energy feature extraction is realized through wavelet packet transform and feature compression is com-pleted based on PCA. Secondly, multi-class one vs. One method of SVM combined with nearest neighbors method carries out the faults classification. And, a synthesis GA is introduced to choose wavelet function and parameters of SVM at the same time. At last, improved SVM is compared with BP, RBF, and PNN through simulation experiment of an analog circuit. The result shows that the method of SVM is much better than others,whose correct rate is 98%.