Capacitive MEMS accelerometer wide range modeling using artificial neural network

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 solve this equation, we use the FEA method. The neural network (NN) uses the Levenberg-Marquardt (LM) method for training the system to have a more accurate response. The designed NN can identify and predict the displacement of the movable mass of accelerometer. The simulation results are very promising.