NDLI: Temperature compensation for pressure sensor based on LS-SVR

Content Provider	IEEE Xplore Digital Library
Author	He Ping Pan Guofeng Li Lin Xia Kewen Zhao Hongdong
Copyright Year	2010
Description	Author affiliation: School of Computer Science and Software Engineering, Hebei University of Technology, Tianjin 300401, China (He Ping; Pan Guofeng; Li Lin; Xia Kewen; Zhao Hongdong)
Abstract	When the pressure sensor is used, temperature changes bring about actual measurement errors . A suitable temperature compensation algorithm can be selected to reduce the additive errors and improve the accuracy of measurement. Support vector machine (SVM) is based on the principle of minimum structural risk, and has high precision. Least squares support vector regression (LS-SVR) is adopted to adjust temperature errors for real measured data. The results show that the algorithm is simple, fast learning, a good anti-interference capability, and a small sample of learning ability. Contrasted by BP networks and RBF networks, compensation accuracy of LS-SVR is significantly improved, and reliability of the pressure sensors is enhanced greatly.
Starting Page	4813
Ending Page	4816
File Size	379664
Page Count	4
File Format	PDF
ISBN	9781424462636
e-ISBN	9787894631046
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-07-29
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Chinese Association of Aut
Subject Keyword	Support vector machines Temperature measurement Temperature sensors Accuracy Least Squares Support Vector Regression(LS-SVR) Temperature Compensation Measurement uncertainty Pressure Sensors Educational institutions Electronic mail Measurement Accuracy
Content Type	Text
Resource Type	Article

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