Anotace:
In this paper, support vector regression (SVR) algorithm is used for compact patch antenna design. By etching three T-shaped slots on the ground plane of a rectangle patch antenna, the current distribution on the ground plane is changed and the resonant frequency is reduced. However, there is no reliable formula between the physical parameters of slots and the resonant frequency for antenna design. In this paper, the SVR algorithm is innovatively used to establish the mapping relationship between four parameters and the resonant frequency. In order to reduce the data samples required to train the SVR model, these four parameters are divided into three groups. This grouping method ensures the reasonable distribution of data samples, and greatly reduces the training data samples and reduces the time to collect data by simulator software. The hyperparameters are optimized by using 10-fold cross validation. 108 antenna models (data samples) with different geometrical and electrical parameters are designed and simulated for the initial dataset. The SVR model is trained on the 75 data samples with the coefficient of determination (R2) of 0.9736 and is tested on the remainder 33 data samples. With the computation of the SVR model, the size of the proposed antenna decreases by 19.18% compared with that of the conventional rectangle patch antenna. The proposed structure is fabricated and measured. The results show that the proposed SVR model has good generalization on the real antenna model.