Journal
APPLIED COMPUTATIONAL ELECTROMAGNETICS SOCIETY JOURNAL
Volume 37, Issue 1, Pages 34-40Publisher
APPLIED COMPUTATIONAL ELECTROMAGNETICS SOC
DOI: 10.13052/2022.ACES.J.370104
Keywords
Artificial Neural Network (ANN); multiband antenna; optimization; surrogate modeling
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This work studies the design optimization process of a multi-band antenna using artificial neural network (ANN) based surrogate model and meta-heuristic optimizers. The results show that the proposed methodology provides a computationally efficient design optimization process for multi-band antennas.
In this work, design optimization process of a multi-band antenna via the use of artificial neural network (ANN) based surrogate model and meta-heuristic optimizers are studied. For this mean, first, by using Latin-Hyper cube sampling method, a data set based on 3D full wave electromagnetic (EM) simulator is generated to train an ANN-based model. By using the ANNbased surrogate model and a meta-heuristic optimizer invasive weed optimization (IWO), design optimization of a multi-band antenna for (1) 2.4-3.6 GHz for ISM, LTE, and 5G sub-frequencies, and (2) 9-10 GHz for X-band applications is aimed. The obtained results are compared with the measured and simulated results of 3D EM simulation tool. Results show that the proposed methodology provides a computationally efficient design optimization process for design optimization of multiband antennas.
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