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Outlier- tolerant fitting and online diagnosis of outliers in dynamic process sampling data series
KTH, School of Computer Science and Communication (CSC), Numerical Analysis and Computer Science, NADA.ORCID iD: 0000-0002-9706-5008
2011 (English)In: 3rd International Conference on Artificial Intelligence and Computational Intelligence, AICI 2011, 2011, 195-203 p.Conference paper, Published paper (Refereed)
Abstract [en]

Outliers as well as outlier patches, which widely emerge in dynamic process sampling data series, have strong bad influence on signal processing. In this paper, a series of recursive outlier-tolerant fitting algorithms are built to fit reliably the trajectories of a non-stationary sampling process when there are some outliers arising from output components of the process. Based on the recursive outlier-tolerant fitting algorithms stated above, a series of practical programs are given to online detect outliers in dynamic process and to identify magnitudes of these outliers as well as outlier patches. Simulation results show that these new methods are efficient.

Place, publisher, year, edition, pages
2011. 195-203 p.
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 7004 LNAI
Keyword [en]
Outlier-Tolerance – Outlier Detection – Magnitude Identification – Non-stationary Signals – Sensors Fault
National Category
Computer and Information Science
Identifiers
URN: urn:nbn:se:kth:diva-62895DOI: 10.1007/978-3-642-23896-3_23Scopus ID: 2-s2.0-80054077967OAI: oai:DiVA.org:kth-62895DiVA: diva2:481301
Conference
View references (9) 3rd International Conference on Artificial Intelligence and Computational Intelligence, AICI 2011. Taiyuan. 24 September 2011 - 25 September 2011
Note

QC 20120123

Available from: 2012-01-20 Created: 2012-01-20 Last updated: 2017-03-29Bibliographically approved

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Meinke, Karl

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf