Aperture coupled dielectric resonator antenna optimisation using machine learning techniques

In this article, various machine learning (ML) algorithms such as Artificial neural network (ANN), Random Forest, XG boost, K nearest neighbor (KNN), and Knowledge-based neural network (KBNN) are used for efficient optimization of dielectric resonator antenna (DRA). ML models are used to predict the...

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Bibliographic Details
Published in:International journal of electronics and communications 2022-09, Vol.154, p.154302, Article 154302
Main Authors: Srivastava, Ayush, Gupta, Harshit, Kumar Dwivedi, Ajay, Kanth Varma Penmatsa, Krishna, Ranjan, Pinku, Sharma, Anand
Format: Article
Language:English
Subjects:
ANN
Aperture Coupling
Dielectric resonator antenna
KBNN
KNN
Machine learning Algorithm
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Summary:In this article, various machine learning (ML) algorithms such as Artificial neural network (ANN), Random Forest, XG boost, K nearest neighbor (KNN), and Knowledge-based neural network (KBNN) are used for efficient optimization of dielectric resonator antenna (DRA). ML models are used to predict the |S11| for a particular set of frequency, resonator height, aperture radius, and resonator radius. Finally, a comparative performance analysis of different ML algorithms has been done with the outcomes of the HFSS EM simulator. Error percentage is in between 1.0% and 7.0% with different ML algorithms. Antenna design is also fabricated and tested. The performance of the fabricated prototype is very close to different ML algorithms and HFSS obtained outcomes.
ISSN:1434-8411
1618-0399
DOI:10.1016/j.aeue.2022.154302