Fault Diagnosis for Wireless Sensor by Twin Support Vector Machine. (24th April 2013)

• Record Type: Journal Article
• Authors: Ding, Mingli; Yang, Dongmei; Li, Xiaobing
• Other Names: Balochian Saeed Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2013(2013)
• Publication Date: 2013-04-24
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2013/718783
• Languages: English
• ISSNs: 1024-123X

Abstract

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.