Compensation of Temperature Drift of MEMS Gyroscope Using BP Neural Network

Author

Zhang, Qintuo ; Tan, Zhenfan ; Guo, Lidong

Author_Institution

Coll. of Autom., Harbin Eng. Univ., Harbin, China

fYear

2009

fDate

19-20 Dec. 2009

Firstpage

1

Lastpage

4

Abstract

In order to solve the temperature change on the impact of MEMS gyroscope output, through self-development MEMS IMU temperature test, we used BP neural network to predict the temperature drift of MEMS gyroscope and compensation it. After using BP neural network to compensate three gyroscopes, we compared the results with the traditional least-squares method that the output error variance respectively reduced from 0.4242, 0.3506 and 0.4335 to 0.0758, 0.1024 and 0.1122. The results indicate the correctness and validity of the method, so as to offer a new method to improve the performance of MEMS gyroscope.

Keywords

backpropagation; compensation; gyroscopes; least mean squares methods; micromechanical devices; neural nets; temperature measurement; BP neural network; MEMS IMU temperature test; MEMS gyroscope; least-squares method; temperature change; temperature drift compensation; Educational institutions; Feedforward systems; Gyroscopes; Micromechanical devices; Multi-layer neural network; Neural networks; Resonant frequency; Temperature dependence; Temperature sensors; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Engineering and Computer Science, 2009. ICIECS 2009. International Conference on

Conference_Location

Wuhan

Print_ISBN

978-1-4244-4994-1

Type

conf

DOI

10.1109/ICIECS.2009.5365140

Filename

5365140