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A. Baharodimehr; A. Abolfazl Suratgar; H. Sadeghi (2009)
Publisher: Elsevier
Journal: Journal of Applied Research and Technology
Languages: English
Types: Article
Subjects: Accelerometer, MEMS, cubic stiffness, neural network., Technology, T, Technology (General), T1-995
This paper presents a nonlinear model for a capacitive microelectromechanical accelerometer (MEMA). System parameters ofthe accelerometer are developed using the effect of cubic term of the folded‐flexure spring. To solve this equation, we use theFEA method. The neural network (NN) uses the Levenberg‐Marquardt (LM) method for training the system to have a moreaccurate response. The designed NN can identify and predict the displacement of the movable mass of accelerometer. Thesimulation results are very promising.
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