Outlier- tolerant fitting and online diagnosis of outliers in dynamic process sampling data series
2011 (English)In: 3rd International Conference on Artificial Intelligence and Computational Intelligence, AICI 2011, 2011, 195-203 p.Conference paper (Refereed)
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.
, Lecture Notes in Computer Science, ISSN 03029743 ; 7004 LNAI
Outlier-Tolerance – Outlier Detection – Magnitude Identification – Non-stationary Signals – Sensors Fault
Computer and Information Science
IdentifiersURN: urn:nbn:se:kth:diva-62895DOI: 10.1007/978-3-642-23896-3_23ScopusID: 2-s2.0-80054077967OAI: oai:DiVA.org:kth-62895DiVA: diva2:481301
View references (9) 3rd International Conference on Artificial Intelligence and Computational Intelligence, AICI 2011. Taiyuan. 24 September 2011 - 25 September 2011
QC 201201232012-01-202012-01-202012-01-23Bibliographically approved