Deep Learning Based Multiresponse Optimization Methodology for Dual-Axis MEMS Accelerometer

被引:1
|
作者
Mattoo, Fahad A. [1 ,2 ]
Nawaz, Tahir [1 ,2 ]
Saleem, Muhammad Mubasher [1 ,2 ]
Khan, Umar Shahbaz [1 ,2 ]
Hamza, Amir [1 ,2 ]
机构
[1] Natl Univ Sci & Technol NUST, Dept Mechatron Engn, Islamabad 44000, Pakistan
[2] Natl Ctr Robot & Automat, Islamabad 44000, Pakistan
关键词
deep neural network; dual-axis MEMS accelerometer; microelectromechanical systems (MEMS); multiresponse optimization; deep learning (DL); neural network;
D O I
10.3390/mi14040817
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
This paper presents a deep neural network (DNN) based design optimization methodology for dual-axis microelectromechanical systems (MEMS) capacitive accelerometer. The proposed methodology considers the geometric design parameters and operating conditions of the MEMS accelerometer as input parameters and allows to analyze the effect of the individual design parameters on the output responses of the sensor using a single model. Moreover, a DNN-based model allows to simultaneously optimize the multiple output responses of the MEMS accelerometers in an efficient manner. The efficiency of the proposed DNN-based optimization model is compared with the design of the computer experiments (DACE) based multiresponse optimization methodology presented in the Literature, which showed a better performance in terms of two output performance metrics, i.e., mean absolute error (MAE) and root mean squared error (RMSE).
引用
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页数:16
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