Resumen

BAHARODIMEHR, A.; ABOLFAZL SURATGAR, A.  y  SADEGHI, H.. Capacitive MEMS accelerometer wide range modeling using artificial neural network. J. appl. res. technol [online]. 2009, vol.7, n.2, pp.185-192. ISSN 2448-6736.

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.

Palabras llave : Accelerometer; MEMS; cubic stiffness; neural network.