This paper presents the synthesis of multiband frequency selective surfaces (FSSs) using supervised machine learning (ML) with the decision tree (DT) algorithm. The proposed FSS structure is composed of an array of metallic patches printed on a dielectric substrate for stopband spatial filtering microwave applications. The shapes of the metallic patches are based on the sunflower (helianthus annus) geometry. In the first step, a parametric analysis is performed to investigate the use of different FSS geometries, including those with circular, annular and corolla integrated patch elements, to compose the sunflower geometry, regarding multiband and polarization independent performances with size reduction. Two bioinspired FSS geometries are synthesized using supervised machine learning with the decision tree algorithm. The random forest (RF) algorithm is used to validate the decision tree algorithm and to confirm the obtained results. The numerical analysis of the proposed FSS geometries is performed using Ansoft Designer software. Prototypes are fabricated and measured. The good agreement observed between simulated and measured results has validated the proposed approach. The use of supervised machine learning with the decision tree algorithm resulted in a particularly efficient and accurate synthesis procedure due to its intuitive implementation and simplified and effective data analysis modelling.

中文翻译：

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使用机器学习和决策树算法合成多频带频率选择表面


本文介绍了使用监督机器学习 (ML) 和决策树 (DT) 算法合成多频带频率选择表面 (FSS)。所提出的 FSS 结构由印刷在介电基板上的金属贴片阵列组成，用于阻带空间滤波微波应用。金属贴片的形状基于向日葵（向日葵）的几何形状。第一步，进行参数分析以研究不同 FSS 几何形状的使用，包括具有圆形、环形和花冠集成贴片元件的几何形状，以构成向日葵几何形状，涉及多波段和偏振独立性能以及尺寸减小。使用监督机器学习和决策树算法合成两种仿生 FSS 几何形状。使用随机森林（RF）算法来验证决策树算法并确认所获得的结果。使用 Ansoft Designer 软件对所提出的 FSS 几何形状进行数值分析。制造并测量原型。模拟结果和测量结果之间观察到的良好一致性验证了所提出的方法。将监督机器学习与决策树算法结合使用，由于其直观的实现和简化且有效的数据分析建模，导致了特别高效和准确的合成过程。