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- W3141617231 abstract "To improve the convenience and efficiency of antenna design, in this article, a novel inverse artificial neural network (ANN) model is proposed in which antenna performance indexes are set as the input and corresponding geometrical variables are set as the output. To solve the multiobjective problem of antenna modeling, the first part of the ANN model involves three parallel and independent branches for <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>S</i> -parameter, gain, and radiation pattern, and the second part outputs a final predicted result. Once the training is completed, the proposed inverse model can provide antenna geometries directly without being repetitively called by an optimization algorithm. Compared with the inverse extreme learning machine and five state-of-art forward models, the proposed model uses a small number of training samples and directly obtains the satisfying values of geometrical variables without any optimization process." @default.
- W3141617231 created "2021-04-13" @default.
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- W3141617231 date "2021-10-01" @default.
- W3141617231 modified "2023-10-17" @default.
- W3141617231 title "Inverse Artificial Neural Network for Multiobjective Antenna Design" @default.
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- W3141617231 doi "https://doi.org/10.1109/tap.2021.3069543" @default.
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