Analog circuit fault diagnosis based on DE-OS-ELM

Shaowei Chen, Minhua Wu, Shuai Zhao

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

3 Citations (Scopus)

Abstract

Extreme Learning Machine has the quality of fast learning speed, good generalization performance, and high diagnostic accuracy. For analog circuit fault diagnosis and health management (PHM) applications, this paper presents the method of online sequential learning machine with differential evolution algorithm to optimize Extreme Learning Machine and improve the diagnostic accuracy and generalization performance effectively.
Original languageEnglish
Title of host publicationProceedings - 2014 7th International Symposium on Computational Intelligence and Design, ISCID 2014
Number of pages5
PublisherIEEE Signal Processing Society
Publication date19 Mar 2015
Pages509-513
Article number7064245
ISBN (Electronic)9781479970056
DOIs
Publication statusPublished - 19 Mar 2015
Externally publishedYes
Event7th International Symposium on Computational Intelligence and Design, ISCID 2014 - Hangzhou, China
Duration: 13 Dec 201414 Dec 2014

Conference

Conference7th International Symposium on Computational Intelligence and Design, ISCID 2014
CountryChina
CityHangzhou
Period13/12/201414/12/2014
SponsorIEEE Nanjing Computational Intelligence Chapter, University of Bristol, Zhejiang Sci-Tech University, Zhejiang University
SeriesProceedings - 2014 7th International Symposium on Computational Intelligence and Design, ISCID 2014
Volume1

Bibliographical note

Publisher Copyright:
© 2014 IEEE.

Keywords

  • analog circuits
  • differential evolution algorithm
  • online sequential learning machine

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