| 基于SVM集成学习的网络故障诊断方法 |
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| 引用本文： | 陈江,单桂军,李正明.基于SVM集成学习的网络故障诊断方法[J].应用声学,2014,22(12). |
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| 作者姓名： | 陈江 单桂军 李正明 |
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| 作者单位： | 镇江高等专科学校 实验实训中心,镇江高等专科学校 实验实训中心,江苏大学 电气信息工程学院 |
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| 基金项目： | 江苏省高校实验室研究会研究课题（JS2012-2），江苏省现代教育技术研究2010年度课题（2010-R-16866），镇江市科技支撑计划项目（GY2012041） |
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| 摘 要： | 针对现有SVM多分类方法在网络故障诊断中识别精度较低的问题,本文提出一种基于二叉树结构和模型二重扰动的SVM集成学习算法。通过集成学习思想提高网络故障诊断的精度。在集成过程中对二叉树结构和核参数进行扰动,加大个体分类器的差异度,提升了诊断模型的泛化性。在实际网络中的诊断实验表明所提的方法较二叉树等其它SVM多分类方法具有更高的诊断精度。
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| 关 键 词： | 支持向量机 二重扰动 集成学习 故障诊断 |
| 收稿时间： | 5/7/2014 12:00:00 AM |
| 修稿时间： | 2014/5/29 0:00:00 |
Network Fault Diagnosis Based on Support Vector Machine Ensemble Learning |
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| SHAN Guijun and LI Zhengming.Network Fault Diagnosis Based on Support Vector Machine Ensemble Learning[J].Applied Acoustics,2014,22(12). |
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| Authors: | SHAN Guijun and LI Zhengming |
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| Institution: | Zhenjiang College,Experimental Training Center,Zhenjiang,Zhenjiang College,Experimental Training Center,Zhenjiang,Jiangsu University,Electrical Information College,Zhenjiang |
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| Abstract: | In network fault diagnosis, the present multi-class support vector machine (SVM) classification methods have low accuracy. The ensemble learning was introduced to improve the diagnosis accuracy. A new ensemble learning algorithm based on the disturbance of both binary tree structure and model (BTSMBag) was proposed. The diversity of individual classifiers was increased by the dual disturbance and the generalization of diagnosis model was improved. The experiment on the real network fault diagnosis shows the proposed method gets higher accuracy. |
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| Keywords: | SVM dual disturbance ensemble learning fault diagnosis |
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