fault diagnosis for wireless sensor by twin support vector machine

Clicks: 137
ID: 184875
2013
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Abstract
Various data mining techniques have been applied to fault diagnosis for wireless sensor because of the advantage of discovering useful knowledge from large data sets. In order to improve the diagnosis accuracy of wireless sensor, a novel fault diagnosis for wireless sensor technology by twin support vector machine (TSVM) is proposed in the paper. Twin SVM is a binary classifier that performs classification by using two nonparallel hyperplanes instead of the single hyperplane used in the classical SVM. However, the parameter setting in the TSVM training procedure significantly influences the classification accuracy. Thus, this study introduces PSO as an optimization technique to simultaneously optimize the TSVM training parameter. The experimental results indicate that the diagnosis results for wireless sensor of twin support vector machine are better than those of SVM, ANN.
Reference Key
ding2013mathematicalfault Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Mingli Ding;Dongmei Yang;Xiaobing Li
Journal journal of power sources
Year 2013
DOI
10.1155/2013/718783
URL
http://dx.doi.org/10.1155/2013/718783
Keywords
civil engineering (general)mathematics engineering (general)

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