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Ver términos de la licenciaDegachi, R., et al. (2024). Comparative analysis of machine learning models to predict rectangular patch antenna dimensions. Journal of Applied Research and Technology; Vol. 22 Núm. 6, 2024; 781-790. Recuperado de https://repositorio.unam.mx/contenidos/4161404
Autor(es)
Degachi, R.; Ghendir, S.
Tipo
Artículo de Investigación
Área del conocimiento
Ingenierías
Título
Comparative analysis of machine learning models to predict rectangular patch antenna dimensions
Fecha
2024-12-11
Resumen
The non-linear relationship between antenna characteristics and their electromagnetic responses made the design and optimization process difficult, in the face of these difficulties, antenna engineers used several techniques, including machine learning, as it has great capabilities that make it a very useful tool that can help researchers in this field. In this paper, four machine learning algorithms: ANN, Random Forest, Decision Tree and SVR are used to predict the dimensions of a rectangular patch antenna, through the utilization of a dataset comprising 3111 simulated samples collected using HFSS Simulation software. The results showed that random forest with 100 estimators exhibited an outstanding performance concerning prediction accuracy, with a mean square error (MSE) of 0.52.
Tema
Antenna design; artificial neural networks; machine learning; patch antenna
Idioma
eng
ISSN
ISSN electrónico: 2448-6736; ISSN: 1665-6423
DOI
https://doi.org/10.22201/icat.24486736e.2024.22.6.2478
