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

fYear

2012

fDate

14-17 Oct. 2012

Firstpage

1800

Lastpage

1805

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;

fLanguage

English

Publisher

ieee

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

Type

conf

DOI

10.1109/ICSMC.2012.6377999

Filename

6377999