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Journal of applied research and technology

versión On-line ISSN 2448-6736versión impresa ISSN 1665-6423

J. appl. res. technol vol.7 no.2 México ago. 2009

 

Capacitive MEMS accelerometer wide range modeling using artificial neural network

 

A. Baharodimehr1, A. Abolfazl Suratgar*2, H. Sadeghi3

 

1 Department of Electrical Engineering, Arak University, Arak, Iran.

2 Department of Electrical Engineering, Tehran Polytechnic University, Tehran, Iran. *a–suratgar@araku.ac.ir, TEL: +98–861–223–2813, FAX:+98–861–222–5946.

3 Department of Physics, Arak University, Arak, Iran.

 

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.

Keywords: Accelerometer, MEMS, cubic stiffness, neural network.

 

RESUMEN

Este trabajo presenta un modelo no lineal para un acelerómetro microelectromecánico de tipo capacitivo (MEMA). Asimismo, en él se desarrollan parámetros de sistema de el acelerómetro utilizando el efecto del término cúbico del resorte de flexion plegado. Para resolver esta ecuación, usamos el método FEA. La red neuronal (RN) usa el método Levenberg–Marquardt (LM) para entrenar al sistema a fin de que tenga una respuesta más exacta. La RN diseñada puede identificar y predecir el desplazamiento de la masa móvil del acelerómetro. Los resultados de la simulación son muy prometedores.

Palabras clave: Acelerómetro, MEMS, rigidez cúbica, red neuronal.

 

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