Paper Title :Comparison of Artificial Intelligence Models to Predict Phases of Patch Array Antenna
Author :Malak Sultan, Alhussein Akoum, Bassam Daya, Elias Rachid
Article Citation :Malak Sultan ,Alhussein Akoum ,Bassam Daya ,Elias Rachid ,
(2024 ) " Comparison of Artificial Intelligence Models to Predict Phases of Patch Array Antenna " ,
International Journal of Electrical, Electronics and Data Communication (IJEEDC) ,
pp. 7-10,
Volume-12,Issue-2
Abstract : The present study introduces some artificial intelligence (AI) model for the lobe direction calculation of an antenna.
Each model utilizes the direction of the lobe as input and the phases of antenna element as outputs. Thus, the lobe direction of
the antenna is readily derived from the trained model's output, which is the phases of the antenna element. While, it’s not easy
to derive the phases of each antenna element from the desired lobe direction. For this purpose, Decision tree Regressor, chain
SVM regressor and direct SVM regressor are three AI models used for simulation. 1280 lobe direction record for 8x1 array
patch antenna were generated by changing the phases of the antenna element, and this set of data were utilized to train each
model. The performance of the trained models is tested by estimating the Mean Absolute Error percentage (MAPE) for each.
The obtained results are compared and direct SVM regressor model have the lowest MAPE value which provides its efficiency
in lobe direction prediction.
Keywords - Artificial Intelligence, Lobe Direction, Microstrip patch array antenna, Machine Learning.
Type : Research paper
Published : Volume-12,Issue-2
DOIONLINE NO - IJEEDC-IRAJ-DOIONLINE-20576
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Copyright: © Institute of Research and Journals
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Published on 2024-05-23 |
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