Abstract：The information included in the non-support vectors is completely ignored for the classification rules of the classical support vector domain description(SVDD), so an all samples SVDD method is proposed in this paper, and it is applied to analog circuit fault diagnosis. The new method is based on Bayes theory and classifier fuzzy fusion strategy. The relative distances of this classifier are weighted by the product of prior probability value and conditional probability value, which are calculated by kernel density estimation. The simulation results show that, compared with the multi-class SVM classifiers, the introduced method improves the fault diagnosis accuracy of analog circuit. Moreover, the all samples SVDD classifier is robust against the changes of classifier parameter.
李传亮, 王友仁, 罗慧, 崔江. 全样本支持矢量数据描述模拟电路故障分类[J]. 电工技术学报, 2012, 27(8): 215-221.
Li Chuanliang, Wang Youren, Luo Hui, Cui Jiang. Analog Circuit Fault Classification Based on All Samples Support Vector Data Description. Transactions of China Electrotechnical Society, 2012, 27(8): 215-221.
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