IJEEDC Comparison of Artificial Intelligence Models to Predict Phases of Patch Array Antenna

Journal Paper

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


	\|		PDF	\|	Viewed - 32	\|	Published on 2024-05-23

Dec. 2024
Submitted Papers	:	80
Accepted Papers	:	10
Rejected Papers	:	70
Acc. Perc	:	12%
Issue Published	:	140
Paper Published	:	1764
No. of Authors	:	4906

Published : Volume-12,Issue-2

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