全样本支持矢量数据描述模拟电路故障分类

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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.

Key words： Analog circuit fault diagnosis multi-class classification support vector data description kernel density estimation

Received: 03 August 2010 Published: 20 March 2014

PACS:

TP277

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	Li Chuanliang
	Wang Youren
	Luo Hui
	Cui Jiang

Cite this article:

Li Chuanliang,Wang Youren,Luo Hui等. Analog Circuit Fault Classification Based on All Samples Support Vector Data Description[J]. Transactions of China Electrotechnical Society, 2012, 27(8): 215-221.

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http://dgjsxb.ces-transaction.com/EN/ OR http://dgjsxb.ces-transaction.com/EN/Y2012/V27/I8/215

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