Resonant Frequency Modeling of Microstrip Antenna Based on Deep Kernel Learning

When modeling and optimizing electromagnetic components, it is the most time consuming for obtaining the training samples with labels from full-wave electromagnetic simulation software. The traditional machine learning (ML) model is usually effective in the training process, unfortunately, its generalization ability is limited for practical application. Inspired by the artificial neural network (ANN) and the Gaussian process (GP) kernel learning model, a deep kernel learning (DKL) model with multiple-nonlinear-mapping layers is proposed based on particle swarm optimization (PSO) algorithm. In this work, correlation characteristics of training samples are transformed by multiple layers, and then they act as inputs of the GP model. Simultaneously, we use PSO to optimize the weights, biases of each mapping layer, and GP hyperparameters, aiming to determine and optimize the DKL model structure. As a result, the proposed DKL is suitable for processing small samples, as well as has the generalization ability of a deep network, which avoids the phenomenon of gradient disappearance to some extent caused by error back propagation (BP) in the deep network and improves the modeling accuracy while effectively ensuring the modeling efficiency. In this study, the performance of the DKL model is evaluated by using the resonant frequencies of two microstrip antennas (MSAs), and the predicted results of the DKL model are compared with those via different modeling methods. The results show that prediction accuracy of the DKL is 18.906% higher than that of the GP, 38.926% higher than the ANN, and 46.660% higher than the neural network ensemble (NNE).