ANALYSIS OF DGS BASED RECTANGULAR PATCH ANTENNA WITH ARTIFICIAL NEURAL NETWORK FOR WIRELESS COMMUNICATION

Chandra Pratap Singh, Rakesh Kumar Dwivedi, Alka Verma, Pinku Ranjan

DOI Number
https://doi.org/10.2298/FUEE2501053S
First page
053
Last page
069

Abstract


This paper presents a defected Rectangular Microstrip Antenna (RMSA) with dimensions of 18x18x1.6 mm³, designed to operate at a center frequency of 7 GHz and within a range of 4.8 GHz to 10.37 GHz for wireless applications. The antenna's performance is evaluated using an Artificial Neural Network (ANN) model, which provides fast predictions of S11 values. The ANN model is trained on data from full-wave electromagnetic simulations. Five ANN algorithms—Adaptive Moment Optimizer, Scaled Conjugate Gradient, Bayesian Black-Box Optimization, Levenberg-Marquardt Algorithm, and Resilient Backpropagation—were used to assess the model's accuracy in predicting S11. The results show that Resilient Backpropagation delivers the best prediction, closely matching both the electromagnetic simulations and experimental measurements, demonstrating strong agreement.


Keywords

Artificial neural network, bandwidth, optimization, microstrip antenna

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