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Cascaded neural network based small array synthesis with robustness to noise.

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Název: Cascaded neural network based small array synthesis with robustness to noise.
Autoři: Dutta, Sagar, Basu, Banani, Talukdar, Fazal Ahmed
Zdroj: International Journal of RF & Microwave Computer-Aided Engineering; Jan2021, Vol. 31 Issue 1, p1-16, 16p
Témata: PLANAR antenna arrays, ARTIFICIAL neural networks, RADIAL basis functions, NEUROPLASTICITY, NOISE, PHYSIOLOGICAL adaptation
Abstrakt: A cascaded neural network approach has been presented in this paper to estimate the excitation for the desired field distribution using a radial basis function neural network (RBFNN). The article has employed an electromagnetic design example consisting of 5 × 5 and 6 × 6 planar antenna array of isotropic sources with inter element‐distance of 0.5λ to show the adaptation of the neural network model in estimating the desired output. A neural network is trained using a dataset of suitable excitation voltages and its corresponding radiation patterns, which proves to be efficient in predicting the excitation voltages required to generate the desired pattern. A set of techniques based on a cascaded neural network is adopted for pattern synthesis using magnitude and phase, magnitude only, and template‐based input data. The robustness of the method has also been tested by considering noise with different SNR levels. The results found in each case have a close fit with the desired pattern. [ABSTRACT FROM AUTHOR]
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Databáze: Complementary Index
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Abstrakt:A cascaded neural network approach has been presented in this paper to estimate the excitation for the desired field distribution using a radial basis function neural network (RBFNN). The article has employed an electromagnetic design example consisting of 5 × 5 and 6 × 6 planar antenna array of isotropic sources with inter element‐distance of 0.5λ to show the adaptation of the neural network model in estimating the desired output. A neural network is trained using a dataset of suitable excitation voltages and its corresponding radiation patterns, which proves to be efficient in predicting the excitation voltages required to generate the desired pattern. A set of techniques based on a cascaded neural network is adopted for pattern synthesis using magnitude and phase, magnitude only, and template‐based input data. The robustness of the method has also been tested by considering noise with different SNR levels. The results found in each case have a close fit with the desired pattern. [ABSTRACT FROM AUTHOR]
ISSN:10964290
DOI:10.1002/mmce.22485