NDLI: Applying Radial Basis Function Neural Network to Data Fusion for Temperature Compensation

Content Provider	IEEE Xplore Digital Library
Author	Zhun Yu You-Yin Jing Ying-Bai Xie Cheng Tian
Copyright Year	2006
Description	Author affiliation: Dept. of Power Eng., North China Electr. Power Univ., Baoding (Zhun Yu; You-Yin Jing; Ying-Bai Xie)
Abstract	In order to decrease the impact of environmental temperature on pressure transducer measurements with temperature compensation, a new method of data fusion based on radial basis function (RBF) neural network was proposed, at the same period, a practical test was carried out with the environmental temperature ranging from 10 to 60 degC and the pressure as 15 kPa. The results of the investigation showed that the relational curves between output voltage of the transducer and environmental temperature was horizontal after compensation, and the convergency of RBF neural network was faster than BP neural network, in addition, the maximum difference of the output voltage before compensation was 9.48 mv while it was 0.03 mv after compensation. The results of the present work implied that the objective of temperature compensation has been achieved essentially, furthermore, RBF neural network was better than BP neural network while used on temperature compensation to pressure transducers and the influence of temperature variation could be greatly reduced
Sponsorship	IEEE Syst., Man and Cybernetics Hebei Univ.
Starting Page	3177
Ending Page	3180
File Size	219787
Page Count	4
File Format	PDF
ISBN	1424400619
DOI	10.1109/ICMLC.2006.258414
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-08-13
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Radial basis function networks Neural networks Transducers Temperature distribution Temperature sensors Capacitive sensors Strain measurement Pressure measurement Electrical resistance measurement Bridge circuits pressure transducer RBF neural network BP neural network temperature compensation
Content Type	Text
Resource Type	Article

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