Title :
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
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;
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
DOI :
10.1109/ICCDA.2010.5541378
