Modeling of Circularly Polarized Antenna Based on Optuna-MBNN

The Optuna-MBNN combines a multi-branch neural network (MBNN) with the Optuna optimization algorithm to overcome limitations in modeling the complex parameters of circularly polarized microstrip antennas. By utilizing the branch structure of MBNN and conducting multi parameter optimization, accurate predictions are achieved for the axial ratio, S-parameter, and gain. The Optuna framework further enhances accuracy by optimizing the network structure and hyperparameters. Experimental results demonstrate the model's high accuracy, with a mean absolute error (MAE) below 0.29.