Machine Learning Technique to Improve an Impedance Matching Characteristic of a Bent Monopole Antenna
We designed the wire monopole antenna bent at three points by applying a machine learning technique to achieve a good impedance matching characteristic. After performing the deep neural network (DNN)-based training, we validated our machine learning model by evaluating mean squared error and R-squar...
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| Auteurs principaux: | , , |
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| Format: | article |
| Langue: | EN |
| Publié: |
MDPI AG
2021
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| Sujets: | |
| Accès en ligne: | https://doaj.org/article/07422380960943528cab54991dc2947c |
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| Résumé: | We designed the wire monopole antenna bent at three points by applying a machine learning technique to achieve a good impedance matching characteristic. After performing the deep neural network (DNN)-based training, we validated our machine learning model by evaluating mean squared error and R-squared score. Considering the mean squared error of about zero and R-squared score of about one, the performance prediction by the resulting machine learning model showed a high accuracy compared with that by the numerical electromagnetic simulation. Finally, we interpreted the operating principle of the antennas with a good impedance matching characteristic by analyzing equivalent circuits corresponding to their structures. The accomplished works in this research provide us with the possibility to use the machine learning technique in the antenna design. |
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