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A new decision tree approach of support vector machine for analog circuit fault diagnosis

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Abstract

This paper proposes a new decision tree approach for analog circuit fault diagnosis using binary support vector machines (BSVMs) that are trained by using different data sets. To evaluate the performance of those different BSVMs, a new criterion is defined by considering the weighted number of fault-pairs that can be separated by one BSVM. Specifically, the weight of each fault-pair is inversely proportional to the number of BSVMs that can separate it. Simulation results by using two example circuits demonstrate that the proposed approach achieves better performance in terms of accuracy and efficiency than existing approaches.

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Authors and Affiliations

  1. Beihang University, Beijing, 100191, China

    Qingfeng Ma, Yuzhu He & Fuqiang Zhou

  2. Huaibei Normal University, Huaibei, 235000, China

    Qingfeng Ma

Authors
  1. Qingfeng Ma
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  2. Yuzhu He
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  3. Fuqiang Zhou
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Correspondence to Qingfeng Ma.

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Ma, Q., He, Y. & Zhou, F. A new decision tree approach of support vector machine for analog circuit fault diagnosis. Analog Integr Circ Sig Process 88, 455–463 (2016). https://doi.org/10.1007/s10470-016-0775-4

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  • DOI: https://doi.org/10.1007/s10470-016-0775-4

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