Enhanced Deep Learning Approach for Multi-parameter Hollow Shaped Cylindrical Dielectric Resonator Antenna Design
DOI:
https://doi.org/10.13052/2024.ACES.J.400802Keywords:
Antenna design, deep learning, dielectric resonator antenna, hollow shaped antennaAbstract
The design of antennas for specific purposes often results in significant time costs due to the lengthy simulation processes required. Adopting deep learning-based approaches in antenna design can offer more efficient solutions. In this study, deep learning methods were applied to accurately and efficiently predict the resonant frequency value of the hollow shaped cylindrical dielectric antenna. For this purpose, a total of 1000 simulations were performed for the considered antenna, and corresponding operational frequencies in 6-12 GHz frequency band were obtained. The data was diversified to search for an optimal solution. A total of 800 simulation results were employed for training, and a series of operations were performed to develop the training model. As a result of these improvements the mean squared error (MSE) was observed to decrease to 0.128. In order to evaluate the performance of the model, the output was obtained by using randomly assigned input parameters. This revealed a difference of 0.49% between the actual result and the model output, which indicates improved prediction accuracy and reliability of the model.
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