Open AccessComparison of Two Methods Basing on Artificial Neural Network and SVM in Fault Diagnosis
Author(s) -
Chunming Li,
Huiling Li
Publication year - 2010
Publication title -
international journal of information engineering and electronic business
Language(s) - English
Resource type - Journals
eISSN - 2074-9023
pISSN - 2074-9031
DOI - 10.5815/ijieeb.2010.01.04
Subject(s) - support vector machine , computer science , artificial neural network , artificial intelligence , pattern recognition (psychology) , reliability (semiconductor) , fault (geology) , classifier (uml) , power (physics) , physics , quantum mechanics , seismology , geology
two diagnosis methods based on a neural network classifier and SVM are proposed for a pulse width modulation voltage source inverter. They are used to detect and identify the transistor open-circuit fault. BP neural network (BPNN) is capable of recognition. However, it has shortcomings obviously. These are just advantages of SVM, which has ability of global search. As an alternative to ANN, SVM can offer higher detection efficiency and reliability. Index Terms -neural network;SVM; fault diagnosis
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