In order to improve the efficiency of antenna optimization design, a surrogate model is often used to replace the full-wave electromagnetic simulation software. Broad learning system (BLS) provides an alternative method for deep structure, aiming to overcome the drawback of excessive time-consuming training process, however, usually not with satisfactory accuracy. In order to further improve the performance of the model, master-apprentice (MA) behavior is proposed in this paper, using the current BLS training results as the priori knowledge, which are taken as fixed features to the next BLS hidden layer for further training. Each MA behavior forms a double BLS structure, which is composed of two parts, the models trained before and after are called master BLS (MBLS) and apprentice BLS (ABLS) respectively. These two subsystems together constitute a master-apprentice BLS (MABLS). Two antenna examples, rectangular microstrip antenna (RMSA) and WLAN dual-band monopole antenna (DBMA), and 10 UCI regression datasets are employed to demonstrate the effectiveness of the proposed model.

为了提高天线优化设计的效率，常采用代理模型代替全波电磁仿真软件。广泛学习系统（BLS）为深度结构提供了一种替代方法，旨在克服过度耗时的训练过程的缺点，但通常精度不令人满意。为了进一步提高模型的性能，本文提出了师徒（MA）行为，以当前BLS训练结果作为先验知识，作为固定特征到下一个BLS隐藏层进行进一步训练. 每个 MA 行为形成一个双 BLS 结构，由两部分组成，前后训练的模型分别称为主 BLS（MBLS）和学徒 BLS（ABLS）。这两个子系统共同构成了一个师徒BLS（MABLS）。两个天线示例，矩形微带天线 (RMSA) 和 WLAN 双频单极天线 (DBMA)，以及 10 个 UCI 回归数据集被用来证明所提出模型的有效性。