Fuzzy Support Vector Machine with a New Fuzzy Membership Function

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Fuzzy Support Vector Machine with a New Fuzzy Membership Function

更新时间：2025-07-09

- Fuzzy Support Vector Machine with a New Fuzzy Membership Function
- Vol. 43, Issue 7, Pages: 40-43(2009)
- 作者机构：
  
  西安交通大学机械工程学院,西安,710049
- 作者简介：
- 基金信息：
- DOI：
  CLC： TB533;TP181
- Online First：10 July 2009，
  
  Published：2009
- 稿件说明：
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Fuzzy Support Vector Machine with a New Fuzzy Membership Function[J]. 2009, 43(7): 40-43.
DOI：

Fuzzy Support Vector Machine with a New Fuzzy Membership Function[J]. 2009, 43(7): 40-43. DOI：

摘要

针对传统支持向量机(SVM)由于样本中存在孤立点数据或噪声而导致的过学习问题

通过分析模糊支持向量机(FSVM)的特点

指出其关键在于如何构建模糊隶属度

为此结合k近邻法思想提出了一种新的隶属度函数构造方法.该方法不仅考虑了样本点到类中心的距离

而且对各样本点排列的紧密程度也进行了估计

与传统SVM相比

它对样本的分类更为清晰和准确.将该方法应用于汽车发动机的实际故障诊断中

结果表明:SVM与普通FSVM的分类正确率较低

而采用新的模糊隶属度的FSVM算法却有较高的识别率

当k为5时分类正确率达到了70.93%

因此验证了该算法的有效性.

Abstract

To solve the over-fitting problem in support vector machine(SVM)

the characteristics of fuzzy support vector machine(FSVM)are analyzed. The key of the FSVM is to construct the fuzzy membership function. Combining with the concept of the k-nearest neighbor algorithm

a new fuzzy membership function is proposed

where the distance between each data point and the center of class

and an affinity among samples are considered simultaneously. Compared with the traditional SVM

the improved algorithm performs more effectively and accurately. This method is employed in automobile engine fault diagnosis

and the analytical results show the good performance of the improved FSVM. The test rate approaches to 70.93% as k is 5.

关键词

Keywords

references

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免疫编程的故障特征构造方法.西安交通大学学报,2008,42(9):1036-1039.

基于支持向量机的发动机故障诊断.西安交通大学学报,2008,42(9):1124-1126.

支持向量机在电力变压器故障诊断中的应用.西安交通大学学报,2007,41(6):722-726.

基于支持向量域描述的多故障诊断动态模型.西安交通大学学报,2007,41(5):593-597.

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