Gross Error Detection of Soft Sensing Data Based on Improved FCM

Ming Ma; Li Biao Zhang

doi:10.4028/www.scientific.net/AMM.427-429.1987

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 427-429Gross Error Detection of Soft Sensing Data Based...

Gross Error Detection of Soft Sensing Data Based on Improved FCM

Abstract:

It is essential to detect the gross errors for improving the precision of soft sensing model. Clustering technique was used to detect gross error in this paper. Based on Fuzzy C-Means clustering algorithm (FCM) and Differential Evolution (DE), the proposed algorithm can detect the gross errors in modeling data for a soft sensor. The numerical experiments result shows that the algorithm is effectively.

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Applied Mechanics and Materials (Volumes 427-429)

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1987-1990

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https://doi.org/10.4028/www.scientific.net/AMM.427-429.1987

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September 2013

Authors:

Ming Ma*, Li Biao Zhang

Keywords:

Differential Evolution, Fuzzy C-Means, Gross Error, Soft Sensor

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