ANN Synthesis Model of Single-Feed Corner-Truncated Circularly Polarized Microstrip Antenna with an Air Gap for Wideband Applications

A computer-aided design model based on the artificial neural network (ANN) is proposed to directly obtain patch physical dimensions of the single-feed corner-truncated circularly polarized microstrip antenna (CPMA) with an air gap for wideband applications. To take account of the effect of the air gap, an equivalent relative permittivity is introduced and adopted to calculate the resonant frequency and Q-factor of square microstrip antennas for obtaining the training data sets. ANN architectures using multilayered perceptrons (MLPs) and radial basis function networks (RBFNs) are compared. Also, six learning algorithms are used to train the MLPs for comparison. It is found that MLPs trained with the Levenberg-Marquardt (LM) algorithm are better than RBFNs for the synthesis of the CPMA. An accurate model is achieved by using an MLP with three hidden layers. The model is validated by the electromagnetic simulation and measurements. It is enormously useful to antenna engineers for facilitating the design of the single-feed CPMA with an air gap.