WSEAS Transactions on Systems
Print ISSN: 1109-2777, E-ISSN: 2224-2678
Volume 13, 2014
Special Issue: Advanced Control Methods: Theory and Application
Editor: F. Neri Classification of Faults in Nuclear Power Plant
Authors: , , , ,
Abstract: In this paper, the performance of traditional Support Vector Machine (SVM) is improved using Genetic Algorithm (GA). GA is used to determine the optimal values of SVM parameters that assure highest predictive accuracy and generalization ability simultaneously. The proposed scheme, called Support Vector Machine Genetic Algorithm (SVM-GA) Scheme, is applied on a beforehand data of a Nuclear Power Plant (NPP) to classify its associated faults. Compared to the standard SVM model, simulation of SVM-GA indicates its superiority when applied on the dataset with unbalanced classes. SVM-GA scheme can gain higher classification with accurate and faster learning speed.
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Keywords: Support Vector Machine (SVM), fault classification, multi fault classification, Genetic Algorithm (GA), machine learning
Pages: 274-284
WSEAS Transactions on Systems, ISSN / E-ISSN: 1109-2777 / 2224-2678, Volume 13, 2014, Art. #26