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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