Performance Investigations of S-shaped RMSA Using Multilayer Perceptron Neural Network for S-Band Applications

Abstract

In this article an S-shaped rectangular microstrip patch antenna (RMSA) is investigated for S-band applications using artificial neural network (ANN). The authors have done the parametric study of different radiating structures to obtain S-shaped RMSA. The size of inserted notches on the radiating patch for achieving wideband operation is computed through multilayer perceptron artificial neural network (MLP-ANN) over a desired range of its performance effecting parameters such as frequency, gain, directivity, antenna efficiency, and radiation efficiency. MLP-ANN model is trained and tested with seven different algorithms. The research found that Levenberg-Marquardt (LM) training algorithm takes less computational time with better accuracy for computation of notches size on radiating patch over a priory defined performance parameters. To verify the work, a prototype of S-shaped RMSA is physically fabricated on foam substrate and tested experimentally. The experimental results are in good agreement with the simulated results that are produced with ANN.