NDLI: Distortion data research of bridge structure health monitoring based on LS-SVM classification

Content Provider	IEEE Xplore Digital Library
Author	Yu Chongchong Zhang Jia Tan Li Wang Jinyan
Copyright Year	2010
Description	Author affiliation: Dept. of Computer and Information, Engineering, BTBU, Beijing, China (Zhang Jia; Tan Li; Wang Jinyan) \|\| Dept. of Computer, and Information, Engineering, BTBU, Beijing, China (Yu Chongchong)
Abstract	As one of the most important monitoring parameters in bridge structure health monitoring and evaluation, distortion data contains abundant information about bridge structure. The paper mainly researches the data classification based on LS-SVM. In order to verify the accuracy of data classification, the stronger generalization capability and faster computation rate of LS-SVM is used with parameters setting, different sample data construction, sample capability and the count of parameters change. The result shows that the classification accuracy of LS-SVM is higher and LS-SVM is a good and effective way to research the classification of distortion data.
Starting Page	118
Ending Page	123
File Size	623046
Page Count	6
File Format	PDF
ISBN	9781424473847
e-ISBN	9781424473861
DOI	10.1109/ICICIS.2010.5534722
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-06-23
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computerized monitoring Least Square Support Vector Machine Data engineering Distortion Capacitive sensors Function approximation Quadratic programming Bridges Condition monitoring Support vector machines Bridge structure healthy monitoring Nonlinear distortion Support vector machine classification
Content Type	Text
Resource Type	Article

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