WLAN monopole antenna design by Siamese convolutional neural network and KNN exploiting Gaussian process

Abstract

In the process of antenna design, surrogate models can generally be used, but modeling requires a large number of samples. Although full wave electromagnetic simulation software can handle this task, obtaining a large number of samples is time-consuming, however too small number of sample may lead to lower accuracy of the trained surrogate model. Inspired by semi-supervised learning methods, this paper uses Siamese convolutional neural networks (SCNN) and K-nearest neighbor (KNN) algorithms to generate highly reliable virtual samples and expand the training sample set, further improving the accuracy and robustness of the surrogate model by exploiting Gaussian process (GP) models. The proposed method is named SCNN-KNN-GP, which is used for the design of WLAN dual band monopole antennas. Moreover, the relationships between the performance of the proposed model and the increased number of virtual samples and the coefficient of the KNN are studied, resulting in a more excellent surrogate model structure.

Keywords

• siamese neural networks
• k-nearest neighbour
• gaussian processes
• monopole antennas
• modelling
• optimization