Computing the pull-in voltage of fixed–fixed micro-actuators by artificial neural network

Microsystem Technologies - Tập 23 - Trang 3537-3546 - 2016
Cevher Ak1, Ali Yıldız2, Ali Akdağlı2, Mustafa Berkan Biçer2
1The Department of Electrical and Electronics Engineering, Toros University, Mersin, Turkey
2The Department of Electrical and Electronics Engineering, Mersin University, Mersin, Turkey

Tóm tắt

In this study, a feed-forward backpropagation (FFBP) artificial neural network (ANN) model based on multilayer perceptron (MLP) for computing the pull-in voltage value of fixed–fixed micro-actuators is presented. The proposed ANN model is trained using the numerous pull-in voltage value results of clamped–clamped actuators, which have various physical properties, simulated by a software that employs the finite element method (FEM). As the presented ANN model has been trained for the simulation data, it implicitly takes into account the fringing field, mid-plane stretching and size effects. The accuracy and robustness of the results obtained by the proposed ANN model are verified by comparing with those of the theoretical ones through the simulation and experimental studies previously presented in the literature. The average percentage errors for training and testing data are found to be 0.30 and 0.40 %, respectively, while the maximum percentage error for literature data is calculated as 1.96 %. These results show that the main advantage of the presented ANN model gives satisfying pull-in voltage results over solution space of the simulations with effortless way.

Tài liệu tham khảo

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