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2023-11-26
Antenna Notch Structure Optimization Using Deep Neural Networks
By
Wenjin Liu,

    Dr. Wenjin Liu

    School of Electronics and Information Engineering

    Liaoning Technical University

    China

    Homepage

Chen Yang,

    Ms. Chen Yang

    School of Electronics and Information Engineering

    Liaoning Technical University

    China

    Homepage

Jingchang Nan,

    Dr. Jingchang Nan

    School of Electronics and Information Engineering

    Liaoning Technical University

    China

    Homepage

Mingming Gao,

    Dr. Mingming Gao

    School of Electronics and Information Engineering

    Liaoning Technical University

    China

    Homepage

Hongliang Niu

    Dr. Hongliang Niu

    School of Electronics and Information Engineering

    Liaoning Technical University

    China

    Homepage

Progress In Electromagnetics Research Letters, Vol. 114, 37-44, 2023
doi:10.2528/PIERL23071902 | Google Scholar

Abstract
To address the stressful and time-consuming problem with the current notched antenna modelling optimization tools, an improved deep multilayer perceptron (DMLP) neural network framework is designed. The method introduces an attention mechanism (Attn) layer to improve the interpretability of the model, uses the leaky ReLU activation function to prevent the gradient from vanishing, and optimizes the structure of the DMLP model using an improved particle swarm algorithm (PSO) to improve the model prediction accuracy. Then, the notch structure geometric parameters of the designed double-notch ultra-wideband (UWB) antenna serve as input to predict the return loss S11 of the antenna. The experimental results show that the method reduces the root mean square error of prediction for S11 by 73.01% compared to the traditional MLP and 64.14% compared to the unimproved DMLP, which provides a solution for modelling notched UWB antennas and helps to optimize the design of this type of antenna.
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Citation
Wenjin Liu, Chen Yang, Jingchang Nan, Mingming Gao, and Hongliang Niu, "Antenna Notch Structure Optimization Using Deep Neural Networks," Progress In Electromagnetics Research Letters, Vol. 114, 37-44, 2023.
doi:10.2528/PIERL23071902
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