Abstract
When using Gaussian process (GP) machine learning as a surrogate model combined with the global optimization method for rapid optimization design of electromagnetic problems, a large number of covariance calculations are required, resulting in a calculation volume which is cube of the number of samples and low efficiency. In order to solve this problem, this study constructs a deep GP (DGP) model by using the structural form of convolutional neural network (CNN) and combining it with GP. In this network, GP is used to replace the fully connected layer of the CNN, the convolutional layer and the pooling layer of the CNN are used to reduce the dimension of the input parameters and GP is used to predict output, while particle swarm optimization (PSO) is used algorithm to optimize network structure parameters. The modeling method proposed in this paper can compress the dimensions of the problem to reduce the demand of training samples and effectively improve the modeling efficiency while ensuring the modeling accuracy. In our study, we used the proposed modeling method to optimize the design of a multiband microstrip antenna (MSA) for mobile terminals and obtained good optimization results. The optimized antenna can work in the frequency range of 0.69–0.96 GHz and 1.7–2.76 GHz, covering the wireless LTE 700, GSM 850, GSM 900, DCS 1800, PCS1900, UMTS 2100, LTE 2300, and LTE 2500 frequency bands. It is shown that the DGP network model proposed in this paper can replace the electromagnetic simulation software in the optimization process, so as to reduce the time required for optimization while ensuring the design accuracy.
Highlights
At present, solving most of the problems concerning antennas relies on full-wave electromagnetic simulation software
Using electromagnetic simulation software to analyze the antenna is complicated and computationally expensive [1]. erefore, many literatures have proposed that artificial neural networks (ANNs) [2], support vector machine (SVMs) [3], and Gaussian process (GP) [4, 5] can be used to analyze antenna problems
As the machine learning (ML) method has developed rapidly in recent decades, GP has a good adaptability to deal with complex problems such as high dimensions, small samples, and nonlinearities, which is easier to implement than SVM and ANN
Summary
At present, solving most of the problems concerning antennas relies on full-wave electromagnetic simulation software. ANN can implement parallel processing, selflearning, and nonlinear mapping, but its structure is relatively complicated, which requires a large amount of electromagnetic simulation data, and it is difficult to determine with poor generalization ability [6]. As the machine learning (ML) method has developed rapidly in recent decades, GP has a good adaptability to deal with complex problems such as high dimensions, small samples, and nonlinearities, which is easier to implement than SVM and ANN. Erefore, PSO is selected to optimize the DGP model in this study, while the mean-squared error of the difference value between the prediction output of the model and the training output is used as the fitness function of PSO. We applied the proposed DGP model to the design of a multiband antenna [18] for the mobile terminal and obtained good optimization results
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