Machine Learning Technique to Improve an Impedance Matching Characteristic of a Bent Monopole Antenna

Machine Learning Technique to Improve an Impedance Matching Characteristic of a Bent Monopole Antenna

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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 Rockers 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 Mugs/Cups/Tumblers with the possibility to use the machine learning technique in the antenna design.