Title :
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
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;
Conference_Titel :
Electronic Manufacturing Technology Symposium (IEMT), 2008 33rd IEEE/CPMT International
Conference_Location :
Penang
Print_ISBN :
978-1-4244-3392-6
Electronic_ISBN :
1089-8190
DOI :
10.1109/IEMT.2008.5507887
