Title :
In-vivo fault prediction for RF generators using variable elimination and state-of-the-art classifiers
Author :
Chandrashekar, Girish ; Sahin, Ferat
Author_Institution :
Electr. & Microelectron. Eng, Rochester Inst. of Technol., Rochester, NY, USA
Abstract :
In this paper we apply two variable elimination algorithms to data obtained from an RF (Radio Frequency) Power Generator Fault Mode for analysis. We use a two wrapper approach using Support Vector Machines (SVM) and Radial Basis Function Networks (RBF) to build an efficient classifier with variable elimination. Comparisons are made for both continuous and discrete datasets.
Keywords :
learning (artificial intelligence); pattern classification; radial basis function networks; support vector machines; RBF; RF power generator; SVM; in-vivo fault prediction; pattern classifier; power generator fault mode; radial basis function network; radiofrequency power generator; support vector machines; two wrapper approach; variable elimination algorithm; Accuracy; Classification algorithms; Generators; Genetic algorithms; Kernel; Radio frequency; Support vector machines; Fault prediction; RF generators; support vector machines; variable elimination;
Conference_Titel :
Systems, Man, and Cybernetics (SMC), 2012 IEEE International Conference on
Conference_Location :
Seoul
Print_ISBN :
978-1-4673-1713-9
Electronic_ISBN :
978-1-4673-1712-2
DOI :
10.1109/ICSMC.2012.6377999
