Abstract
Considering the importance of approximating the pull-in instability occurrence through determining its respective threshold voltage in nano-structures, this work proposes the use of an effective Multi-Layer Perceptron (MLP). Neural Network (NN) and Support Vector Regression (SVR) methods, both having excellent capabilities in estimating data and its respective regression, are considered. To estimate the pull-in voltage of nanostructures 500 data points are used for training, validation, and test procedures where the pull-in voltage and nanostructure characteristics are set as the target as inputs. The pull-in voltage values are determined using the Step by Step Linearization Method (SSLM) and Galerkin modal expansion method. The MLP employs a feed-forward back-propagation approach with two hidden layers containing 10 and 8 neurons. SVR with a Radial Basis Function (RBF) kernel is also utilized. Comparing the two methods, MLP demonstrates good capability in estimating pull-in voltage, with NN showing effective performance in determining nanostructure pull-in voltage. Also, the capability of the MLP method has been evaluated by comparing with the presented results of previous studies, which indicated the competence of the MLP method in predicting the pull-in voltage of nano-beam switches.Author details: Kindly check and confirm whether the corresponding author is correctly identified.It is correct.
Original language | English |
---|---|
Pages (from-to) | 237-243 |
Number of pages | 7 |
Journal | International Journal of Information Technology (Singapore) |
Volume | 16 |
Issue number | 1 |
DOIs | |
Publication status | Published - Dec 11 2023 |
Keywords
- MLP neural network
- Machine learning
- Nano actuator
- Pull-in instability
- Support vector regression
ASJC Scopus subject areas
- Information Systems
- Computer Science Applications
- Computer Networks and Communications
- Computational Theory and Mathematics
- Artificial Intelligence
- Applied Mathematics
- Electrical and Electronic Engineering