CUDA-based PSO-trained neural network for computation of resonant frequency of circular microstrip antenna

Chen, Feng; Tian, Yu bo

doi:10.1504/ijcse.2017.084153

Cited by 2 publications

(1 citation statement)

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“…Chen et al . [12] used PSO parallelization to accelerate ANN training and simulate the resonant frequency of rectangular MSAs under the unified device architecture. Chen et al .…”

Section: Introductionmentioning

confidence: 99%

Design of monopole antennas based on progressive Gaussian process

Xie

Meng

Tian

et al. 2022

Int. J. Microw. Wireless Technol.

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Electromagnetic simulation software has become an important tool for antenna design. However, high-fidelity simulation of wideband or ultra-wideband antennas is very expensive. Therefore, antenna optimization design by using an electromagnetic solver may be limited due to its high computational cost. This problem can be alleviated by the utilization of fast and accurate surrogate models. Unfortunately, conventional surrogate models for antenna design are usually prohibitive because training data acquisition is time-consuming. In order to solve the problem, a modeling method named progressive Gaussian process (PGP) is proposed in this study. Specially, when a Gaussian process (GP) is trained, test sample with the largest predictive variance is inputted into an electromagnetic solver to simulate its results. After that, the test sample is added to the training set to train the GP progressively. The process can incrementally increase some important trusted training data and improve the model generalization performance. Based on the proposed PGP, two monopole antennas are optimized. The optimization results show effectiveness and efficiency of the method.

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“…Chen et al . [12] used PSO parallelization to accelerate ANN training and simulate the resonant frequency of rectangular MSAs under the unified device architecture. Chen et al .…”

Section: Introductionmentioning

confidence: 99%

Design of monopole antennas based on progressive Gaussian process

Xie

Meng

Tian

et al. 2022

Int. J. Microw. Wireless Technol.

View full text Add to dashboard Cite

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Antenna Optimization Design Based on Deep Gaussian Process Model

Zhang

Tian

Xie

2020

International Journal of Antennas and Propagation

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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.

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CUDA-based PSO-trained neural network for computation of resonant frequency of circular microstrip antenna

Cited by 2 publications

References 0 publications

Design of monopole antennas based on progressive Gaussian process

Design of monopole antennas based on progressive Gaussian process

Antenna Optimization Design Based on Deep Gaussian Process Model

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