The application of RBF neural network in the compensation for temperature drift of the silicon pressure sensor

Author

Chuan, Yang ; Chen, Li ; Chao, Zhang

Author_Institution

State Key Lab. for Manuf. Syst. Eng., Xi´´an Jiaotong Univ., Xi´´an, China

Volume

2

fYear

2010

fDate

25-27 June 2010

Abstract

Temperature drift is the important factor of the precision of diffused silicon pressure sensor, so author uses software to compensate for it to improve the precision of the sensor. At the data base of the temperature characteristic experiment of diffused silicon pressure sensor, author proposes to use RBF neural network to establish temperature drift compensated model with regression analysis. Compared with two-dimension regression analysis, RBF neural network can improve the precision of the model distinctly.

Keywords

measurement uncertainty; neural nets; pressure sensors; radial basis function networks; temperature; RBF neural network; pressure sensor; regression analysis; temperature drift compensation; Bridge circuits; Intelligent sensors; Neural networks; Resistors; Sensor phenomena and characterization; Sensor systems and applications; Silicon; Temperature sensors; Thermal stresses; Voltage; RBF neural network; diffused silicon pressure sensor; temperature drift;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Design and Applications (ICCDA), 2010 International Conference on

Conference_Location

Qinhuangdao

Print_ISBN

978-1-4244-7164-5

Electronic_ISBN

978-1-4244-7164-5

Type

conf

DOI

10.1109/ICCDA.2010.5541378

Filename

5541378