Bagged ensemble of Fuzzy C-Means classifiers for nuclear transient identification

Piero Baraldi (1) , Roozbeh Razavi-Far (1) , Enrico Zio (2)

1 Dipartimento di Energia [Milano]
2 SSEC - Chaire Sciences des Systèmes et Défis Energétiques EDF/ECP/Supélec

This paper presents an ensemble-based scheme for nuclear transient identification. The approach adopted to construct the ensemble of classifiers is bagging; the novelty consists in using supervised fuzzy C-means (FCM) classifiers as base classifiers of the ensemble. The performance of the proposed classification scheme has been verified by comparison with a single supervised, evolutionary-optimized FCM classifier with respect of the task of classifying artificial datasets. The results obtained indicate that in the cases of datasets of large or very small sizes and/or complex decision boundaries, the bagging ensembles can improve classification accuracy. Then, the approach has been applied to the identification of simulated transients in the feedwater system of a boiling water reactor (BWR).

Mots clés: Classification; Fuzzy C-means (FCM) clustering; Ensemble; Bagging; Transient identification; BWR nuclear power plant

Domaines: Energie électrique

Piero Baraldi, Roozbeh Razavi-Far, Enrico Zio. Bagged ensemble of Fuzzy C-Means classifiers for nuclear transient identification. Annals of Nuclear Energy, 2011, 38 (5), pp.1161-1171. ⟨10.1016/j.anucene.2010.12.009⟩. ⟨hal-00609529⟩