Outlier-tolerant fitting and online diagnosis of outliers in dynamic process sampling data series

Authors:
Shaolin Hu;Xiaofeng Wang;Karl Meinke;Huajiang Ouyang
Affiliations:
State Key Laboratory of Astronautics, Xi'an Satellite Control Center, Xi'an, China and Xi'an University of Technology, Xi'an, China;Xi'an University of Technology, Xi'an, China;NADA, Royal Institute of Technology, Stockholm, Stockholm, Sweden;Liverpool University, Liverpool, UK
Venue:
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part III
Year:
2011

Citing 3
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Abstract

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.