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Support vector regression models of reflectarray unit cell in a geometrical 4-D parallelotope domain around a rectangle of stability
dc.contributor.author | Rodriguez Prado, Daniel | |
dc.contributor.author | López Fernández, Jesús Alberto | |
dc.contributor.author | Arrebola Baena, Manuel | |
dc.date.accessioned | 2023-05-08T09:50:36Z | |
dc.date.available | 2023-05-08T09:50:36Z | |
dc.date.issued | 2023 | |
dc.identifier.citation | IEEE Transactions on Antennas and Propagation (2023); doi:10.1109/TAP.2023.3266502 | |
dc.identifier.issn | 0018-926X | |
dc.identifier.issn | 1558-2221 | |
dc.identifier.uri | http://hdl.handle.net/10651/67690 | |
dc.description.abstract | 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. | spa |
dc.description.sponsorship | This work was supported in part by the Ministerio de Ciencia, Innovación y Universidades under Project IJC2018-035696-I, in part by MICIN/AEI/10.13039/501100011033 under Project PID2020-114172RB-C21 (ENHANCE-5G), in part by the NextGenerationEU under the Recovery Plan for Europe under Project TED2021-130650B-C22, and in part by the Gobierno del Principado de Asturias under Project AYUD/2021/51706. | spa |
dc.language.iso | eng | spa |
dc.publisher | IEEE | spa |
dc.relation.ispartof | IEEE Transactions on Antennas and Propagation | spa |
dc.rights | CC Reconocimiento – No Comercial – Sin Obra Derivada 4.0 Internacional | * |
dc.rights | © 2023 IEEE. | |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Dual band | spa |
dc.subject | machine learning | spa |
dc.subject | orthotope | spa |
dc.subject | parallelotope | spa |
dc.subject | reflectarray antenna | spa |
dc.subject | support vector regression (SVR) | spa |
dc.subject | surrogate model | spa |
dc.subject | wideband | spa |
dc.title | Support vector regression models of reflectarray unit cell in a geometrical 4-D parallelotope domain around a rectangle of stability | spa |
dc.type | journal article | spa |
dc.identifier.doi | 10.1109/TAP.2023.3266502 | |
dc.relation.projectID | IJC2018-035696-I | spa |
dc.relation.projectID | PID2020-114172RB-C21 (ENHANCE-5G) | spa |
dc.relation.projectID | TED2021-130650B-C22 | spa |
dc.relation.projectID | AYUD/2021/51706 | spa |
dc.relation.publisherversion | https://doi.org/10.1109/TAP.2023.3266502 | |
dc.rights.accessRights | open access | spa |
dc.type.hasVersion | AM |
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