An Efficient Knowledge-Based Artificial Neural Network for the Design of Circularly Polarized 3-D-Printed Lens Antenna

An efficient knowledge-based artificial neural network (KBANN) is proposed, and it is used for the design of circularly polarized (CP) lens antenna in this article. In this KBANN, forward neural network (FNN) and inverse neural network (INN) are included. In this model, INN is the major component to predict the antenna structure parameters. As multiple performance indices are required, INN requires a large number of training samples to deduce complex mapping relationship. To solve this problem, FNN is introduced to provide prior knowledge for INN. FNN generates a huge training dataset for INN training, and then the trained INN can directly output the geometric parameters by feeding the target electromagnetic responses as input. This article solves the problem of multiple performance indices in antenna design, and a CP lens antenna with wideband, good axial ratio, and high gain is designed and fabricated to verify the effectiveness of the KBANN model.

Automated summary

An efficient knowledge-based artificial neural network (KBANN) is proposed for the design of circularly polarized (CP) lens antenna. The KBANN model includes a forward neural network (FNN) and an inverse neural network (INN), where INN is the main component used to predict the antenna structure parameters. By introducing FNN to provide prior knowledge for INN, the problem of multiple performance indices in antenna design is solved. A CP lens antenna with wideband, good axial ratio, and high gain is designed and fabricated to demonstrate the effectiveness of the KBANN model.