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.