为了解决支持向量机在模拟电路中诊断时间长的问题,同时提高故障的诊断精度,提出一种基于纠错码支持向量机的模拟电路故障诊断方法.首先采用模糊C-均值算法对训练样本两两聚类,根据聚类二叉树得到纠错码矩阵；然后按码矩阵的编码设计基于支持向量机的多类故障分类器,对样本进行分组训练和测试；最后对测试向量进行解码得到诊断结果.实验结果表明,文中方法的诊断精度优于传统支持向量机和BP神经网络方法,同时有效地降低了基于支持向量机多类分类的故障诊断时间.%In order to reduce fault diagnosis time for analog circuit based on SVM method and improve the accuracy of fault diagnosis, this paper proposes a method of analog circuits diagnosis by combining error-correcting output code (ECOC) and support vector machine (SVM). Firstly, Fuzzy C-means (FCM) algorithm is employed to cluster the fault samples, and then ECOC matrix is obtained from binary tree. Secondly, the multi-class fault classifiers are designed using SVMs to train and test samples corresponding to ECOC matrix. Finally, test results are decoded by a decoding method. The experimental results show that the diagnosis accuracy of the proposed fault classifier is superior to that of the conventional SVM and BP neural network, and the diagnosis time of the new method is lower than that of the multi-class method based on SVM.
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