A new fuzzy membership function for FSVM and its application in machinery fault diagnosis

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

In this paper, a new fuzzy membership function for fuzzy support vector machine is presented. It provides an effective approach to deal with the over-fitting problem when outliers exist in the training data set. Combining with the concept of the K-nearest neighbor algorithm, we give a definition of the new fuzzy membership function. Then, fuzzy support vector machine with some improvements is successfully applied in machinery fault diagnosis and some engineering experimental results show the good performance of the present approach.

Original languageEnglish
Title of host publicationProceedings - 2012 8th International Conference on Natural Computation, ICNC 2012
Pages35-39
Number of pages5
DOIs
StatePublished - 2012
Event2012 8th International Conference on Natural Computation, ICNC 2012 - Chongqing, China
Duration: 29 May 201231 May 2012

Publication series

NameProceedings - International Conference on Natural Computation
ISSN (Print)2157-9555

Conference

Conference2012 8th International Conference on Natural Computation, ICNC 2012
Country/TerritoryChina
CityChongqing
Period29/05/1231/05/12

Keywords

  • fuzzy membership function
  • fuzzy support vector machine
  • machinery fault diagnosis

Fingerprint

Dive into the research topics of 'A new fuzzy membership function for FSVM and its application in machinery fault diagnosis'. Together they form a unique fingerprint.

Cite this