Title: To predict the characteristic impedance of the microstrip transmission line using supervised machine learning regression techniques

Authors: Mohammad Ahmad Ansari; Krishnan Rajkumar; Poonam Agarwal

Addresses: Microsystems Lab, School of Computer & Systems Sciences, Jawaharlal Nehru University, New Delhi, Delhi, India ' School of Engineering, Jawaharlal Nehru University, New Delhi, Delhi, India ' Microsystems Lab, School of Computer & Systems Sciences, Jawaharlal Nehru University, New Delhi, Delhi, India

Abstract: In this paper, supervised machine learning regression techniques: Support Vector Machine (SVM), Random Forest and Deep Neural Network (DNN) models, are demonstrated to predict the characteristic impedance of the microstrip transmission line. Here, microstrip transmission line width, substrate height and substrate dielectric constant are taken as the input and characteristics impedance as the output parameter. To train the models, the data set is created using microstrip transmission line analytical models. DNN models are developed using Feed-forward Back-propagation learning algorithm, where 'adam' is used as optimiser and 'relu' as the activation function. The regression predictive model of SVM and Random Forest model of ensemble learning using bagging technique are developed. It is found that minimum MSE of DNN model is 0.04191 with high execution time 1114.179655 sec, whereas SVM model shows low execution time of 0.8327 sec with MSE of 0.49. Random Forest model showed the MSE of 0.14 with execution time 1.4296 sec.

Keywords: microstrip transmission line; ANN; DNN; support vector machine; random forest; adam; relu; modelling.

DOI: 10.1504/IJCAT.2023.133037

International Journal of Computer Applications in Technology, 2023 Vol.72 No.2, pp.96 - 107

Received: 27 Jun 2022
Received in revised form: 29 Sep 2022
Accepted: 26 Oct 2022
Published online: 27 Aug 2023 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article