Capacitive MEMS accelerometer wide range modeling using artificial neural network

被引：0
|
作者：
Baharodimehr, A. [2 ]
Suratgar, A. Abolfazl [1 ]
Sadeghi, H. [3 ]
机构：
[1] Tehran Polytech Univ, Dept Elect Engn, Tehran, Iran
[2] Arak Univ, Dept Elect Engn, Arak, Iran
[3] Arak Univ, Dept Phys, Arak, Iran
来源：
JOURNAL OF APPLIED RESEARCH AND TECHNOLOGY | 2009年 / 7卷 / 02期
关键词：
Accelerometer; MEMS; cubic stiffness; neural network;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
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.
引用
收藏
页码：185 / 192
页数：8
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