SVM Ensemble Algorithm Based on Double Disturbance Mechanism and Its Application in Analog Circuit Fault Diagnosis

MA Chao; CHEN Xi-hong; YAO Yi-ling; HAN Ming

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SVM Ensemble Algorithm Based on Double Disturbance Mechanism and Its Application in Analog Circuit Fault Diagnosis

更新时间：2026-09-04

- SVM Ensemble Algorithm Based on Double Disturbance Mechanism and Its Application in Analog Circuit Fault Diagnosis
- Vol. 28, Issue 3, Pages: 17-22(2011)
- 作者机构：
  
  1. 1 空军工程大学机构导弹学院, 陕西三原,713800
  2. 2 空军驻210所军事代表室,陕西,西安,710065
- 作者简介：
- 基金信息：
- DOI：
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- Published：2011
- 稿件说明：
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马超, 陈西宏, 姚懿玲, 韩明. 模拟电路故障诊断的双重扰动支持向量机集成方法[J]. 微电子学与计算机, 2011, 28(3): 17-22.

MA Chao, CHEN Xi-hong, YAO Yi-ling, et al. SVM Ensemble Algorithm Based on Double Disturbance Mechanism and Its Application in Analog Circuit Fault Diagnosis[J]. 2011, 28(3): 17-22.
马超, 陈西宏, 姚懿玲, 韩明. 模拟电路故障诊断的双重扰动支持向量机集成方法[J]. 微电子学与计算机, 2011, 28(3): 17-22. DOI：

MA Chao, CHEN Xi-hong, YAO Yi-ling, et al. SVM Ensemble Algorithm Based on Double Disturbance Mechanism and Its Application in Analog Circuit Fault Diagnosis[J]. 2011, 28(3): 17-22. DOI：

摘要

为进一步提高模拟电路故障诊断准确率

提出一种特征和模型参数双重扰动的集成支持向量机新算法.首先在集合覆盖思想下设计基于混沌蚁群算法的属性约简算法将特征样本空间划分成若干子空间

然后针对每个子空间

在"低偏差区域"内进行模型参数扰动

经过两次多数投票法得到最终集成结果.故障诊断实例表明

该方法比多分类支持向量机、Attribute Bagging(AB)算法、Bagging方法等具有更好的故障诊断率.

Abstract

To improve the diagnosis accuracy in analog circuit

a SVM ensemble algorithm based on double disturbance mechanism of attribute and model parameter is proposed. Firstly

aattribute reduction algorithm based on chaos-ant optimization algorithm is presented under method of set-covering

to partition attribute space into many sub-spaces. And then in each subspace

the model parameter is disturbanced in the region of Low-Bias. Finally

the final Ensemble result is obtained by using two times of majority-voting method. The diagnosis instance indicates that the proposed algorithm performace better than many other algorithms

take multi-SVM

AB algorithm

Bagging and so on for example.

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