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中文摘要：
针对模拟电路的故障诊断和定位问题，提出了一种基于支持向量机分类器（SVC）和最近邻分类器（NNs）的模拟电路故障诊断新策略，利用SVC解决高维故障样本的分类问题，而采用NNs解决故障样本间的重叠问题。首先建立“1-v—r”结构的SVC对电路故障样本进行训练，并根据训练参数构建故障字典；其次，在测试阶段，根据算法决定利用SVC或NNs对未知样本进行测试。本文设计的故障分类器方法简单，结构确定，通过对两个模拟电路的实验表明，所提出的方法性能要优于常规的“1-v—r”支持向量机分类器；与“1-v-1”支持向量机分类器的诊断性能较为接近，但测试时间较后者显著减少，较为适合模拟电路的故障诊断。
英文摘要：
Focusing on the issue of analog circuit fault diagnosis and location, this paper proposes a novel strategy of fault diagnosis by combing the support vector machine classifier （SVC） and the nearest neighbor （NN） classifier. The SVC is used to classify the high-dimension fault samples, and the NN classifier is used to recognize the overlapped samples. Firstly, the ＂1-v-r＂ SVC is utilized to train the fault samples; after training, the parameters are stored as a fault dictionary. Secondly, in the test stage, the SVC or the NN classifier is employed, depending on the results of the algorithm, to diagnose the unknown sample. The classifier proposed here has a simple but fixed structure. The simulation for two analog circuits reveals that the performance of the proposed fault classifier is superior to that of the conventional ＂1-v-r＂ SVC, close to that of the ＂1-v-1＂ SVC, and the test time consumed is less than that of the ＂1-v-1＂ SVC remarkably; the proposed method is suitable for diagnosing analogue circuits.
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