基于改进SVM的模拟电路故障诊断

针对模拟电路的故障诊断和定位问题，提出了一种改进支持向量机（suppon Vector Machine，SVM）故障诊断方法。通过在标准SVM中加入了对数据流形局部分布的约束，设计了一种依赖于数据分布的新型SVM。相对于标准SVM方法而言，新方法有效融合了数据分布的先验信息，提高了模型的诊断精度。将其用于模拟电路的故障诊断，验证了所提方法的有效性。

Analog Circuit Fault Diagnosis Based on Improved SVM

Focusing on the problem of analog circuit fault diagnosis and location, an improved support vector machine （SVM） was proposed for fault diagnosis. A new type of SVM lying on data distribution was designed by joining the con- straint of distribution of the data manifold. Compared to standard SVM, the proposed method effectively combined prior dis- tribution information of the data to increase the diagnosis accuracy of the model. The simulation results showed the effec- tiveness of the algorithm.