Optimisation of Star fractal antennas by ANN for Multiband applications
DOI:
https://doi.org/10.5935/jetia.v12i61.4553Abstract
In antenna engineering, artificial Neural Networks (ANNs) are increasingly utilized to optimize key performance parameters.Here, An ANN-based approach is employed to enhance the return loss of a modified star fractal antenna operating across the 5.6 to 8.4 GHz frequency band. Multiple geometric parameters are considered as inputs in this model, e. g. the feed line width, the radius of the central circular slot, and also the radius of the neighboring smaller circular slots. The ANN outputs the real and imaginary parts of the return loss, which are then utilized as optimization objectives. To get the dataset for training and validating, electromagnetic simulations were run with CST Microwave Studio. Although the training data was very small, our developed ANN model has come up with quite accurate forecasts and even managed to allocate highly optimized return loss characteristics in a very short period of time
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