Machine learning‐driven design of dual‐band antennas using PGGAN and enhanced feature mapping

Abstract This paper presents a systematic antenna design methodology that integrates machine learning, leveraging the progressive growth technique of Progressive Growing of GANs (PGGAN) to generate images of various dual‐band PIFA‐like antenna structures. The process involves using data augmentation...

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Dades bibliogràfiques

Autors principals:	Lung‐Fai Tuen, Ching‐Lieh Li, Yu‐Jen Chi, Chien‐Ching Chiu, Po Hsiang Chen
Format:	Artigo
Idioma:	Inglês
Publicat:	Wiley 2024-12-01
Col·lecció:	IET Microwaves, Antennas & Propagation
Matèries:	dual‐band antenna hough transform Latin hypercube sampling PGGAN WGAN‐GP
Accés en línia:	https://doi.org/10.1049/mia2.12534
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