Nonlinear modeling of a capacitive MEMS accelerometer using neural network

Author

Bahadorimehr, A.R. ; Hamidon, M. ; Hezarjaribi, Y.

Author_Institution

Electr. & Electron. Dept., Univ. Putra Malaysia, Serdang, Malaysia

fYear

2008

fDate

4-6 Nov. 2008

Firstpage

1

Lastpage

6

Abstract

This paper presents a nonlinear model for a capacitive Microelectromechanical accelerometer (MEMA). System parameters of the accelerometer are developed using the effect of cubic term of the folded-flexure spring. To solving this equation we use FEA method. The neural network (NN) uses Levenberg-Marquardt (LM) method for training the system to have more accurate response. The designed NN can identify and predict the displacement of movable mass of accelerometer. The simulation results are very promising.

Keywords

accelerometers; capacitive sensors; electrical engineering computing; finite element analysis; microsensors; neural nets; FEA method; Levenberg-Marquardt method; capacitive MEMS accelerometer; folded-flexure spring; microelectromechanical accelerometer; neural network; nonlinear modeling; Accelerometers; Circuit faults; Fault diagnosis; Field programmable gate arrays; Logic testing; Manufacturing; Micromechanical devices; Neural networks; Table lookup; Tiles;

fLanguage

English

Publisher

ieee

Conference_Titel

Electronic Manufacturing Technology Symposium (IEMT), 2008 33rd IEEE/CPMT International

Conference_Location

Penang

ISSN

1089-8190

Print_ISBN

978-1-4244-3392-6

Electronic_ISBN

1089-8190

Type

conf

DOI

10.1109/IEMT.2008.5507887

Filename

5507887