Design of Miniaturized-Element Frequency Selective Surface Using Neural Networks

Liu, Cong; Wang, Yuxiang; Yuan, Yueyi; Yang, Guohui; Wu, Qun; Zhang, Kuang

doi:10.1109/iceict55736.2022.9909479

Cited by 2 publications

(3 citation statements)

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“…Throughout the studies of ANN based analysis and synthesis, three different metrics are used to evaluate the accuracy of the proposed regression model. The metrics for the calculation of error values between the predicted outputs of the ANN models and the target output values of the dataset are mean absolute error (MAE), mean square error (MSE) and root mean square error (RMSE), as given in the Equations ( 25)- (27), respectively. During simulations, it is set that either these error values cannot exceed 1 Â 10 À5 or maximum iteration number is obtained.…”

Section: Performance Evaluation With Neural Network Algorithmsmentioning

confidence: 99%

“…26, a deep learning method for FSS inverse design is proposed, where the dimension of the input is reduced by using Fourier subspace to represent the most salient features of the desired S ‐parameter performance. In another work, a neural network‐based method is used for obtaining the relationship between coupling elements and structural parameters of a single band FSS so that the transmission coefficient can be obtained directly without full‐wave simulations 27 . In Ref.…”

Section: Introductionmentioning

confidence: 99%

“…In another work, a neural network-based method is used for obtaining the relationship between coupling elements and structural parameters of a single band FSS so that the transmission coefficient can be obtained directly without full-wave simulations. 27 In Ref. 28, a fast accurate inverse method of designing functional metasurfaces based on transfer learning is proposed, which can generate metasurface patterns monolithically from input phase profiles for specific functions.…”

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confidence: 99%

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Triple band frequency selective surface design for 5G mm‐wave communication with artificial neural networks

Şahin,

Serinken,

Vural

et al. 2024

Int J Numerical Modelling

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High‐performance frequency selective surfaces (FSSs) have gained attention for their spatial filtering characteristics in 5G communication systems. In this work, we propose an efficient and accurate design methodology for the FSS. Three different artificial neural network methods (ANN) are employed, and their performances are compared for analysis and synthesis purposes. Results show that GRNN has the highest performance for both training and test phase of ANN based FSS analysis and synthesis. A novel, compact, low‐profile triple band FSS unit cell is introduced, and the working mechanism is described. By applying ANN based design procedure, the unit cell dimensions to resonate at the 5G mm‐wave frequency band is extracted. A unit cell with the extracted physical dimensions is simulated with a full‐wave analysis tool. The simulation results show that the FSS has the filtering feature at the predetermined mm‐wave frequencies of the 5G communication. The prototype of the FSS is fabricated, as well. The simulations are verified experimentally with measurement results. The results show that proposed ANN based analysis and synthesis method can be an effective tool for the design of FSS band‐pass filter for 5G applications.

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Section: Performance Evaluation With Neural Network Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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