Support vector regression models of reflectarray unit cell in a geometrical 4-D parallelotope domain around a rectangle of stability
In this work, surrogate models based on support vector regression (SVR) of a multiresonant unit cell in a geometrical 4-D parallelotope domain are trained and used in a reflectarray antenna design. The multiple sharp resonances of the unit cell prevent a suitable training process in the whole orthot...
| Autores: | , , |
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| Tipo de documento: | artigo |
| Data de publicação: | 2023 |
| País: | España |
| Recursos: | Universidad de Oviedo (UNIOVI) |
| Repositório: | RUO. Repositorio Institucional de la Universidad de Oviedo |
| Idioma: | inglês |
| OAI Identifier: | oai:digibuo.uniovi.es:10651/67690 |
| Acesso em linha: | http://hdl.handle.net/10651/67690 https://dx.doi.org/10.1109/TAP.2023.3266502 |
| Access Level: | Acceso aberto |
| Palavra-chave: | Dual band machine learning orthotope parallelotope reflectarray antenna support vector regression (SVR) surrogate model wideband |
| Resumo: | In this work, surrogate models based on support vector regression (SVR) of a multiresonant unit cell in a geometrical 4-D parallelotope domain are trained and used in a reflectarray antenna design. The multiple sharp resonances of the unit cell prevent a suitable training process in the whole orthotope defined by the available degrees of freedom (DoFs). Thus, a strategy to improve the training process and obtain highly accurate models is devised. It consists in defining a parallelotope around a rectangle of stability, which is in turn defined at a lower dimensionality. The SVR models with four geometrical DoF obtained in this parallelotope are shown to provide highly accurate results for the design of a large contoured-beam reflectarray for space applications. The direct optimization with the surrogate models allows to improve the cross-polarization performance by several decibels while considerably increasing computational performance. Furthermore, compared to lower dimensionality models, the 4-D models offer better results when applied to wideband and dual-band reflectarray direct optimization. |
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