The method of array antenna constructive synthesis on the basis of neural network approach
The method to decision constructive synthesis of array antennas was conducted. The method usefull when antenna elements can be in discreste states (for example: active element, passive element, excluded item, active element with discrete nominal of output power e.t.c). The method is based on neural...
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EDP Sciences
2019-01-01
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doaj-8d9f86925aa64b99a0528599f5c880af2021-02-02T08:46:35ZengEDP SciencesITM Web of Conferences2271-20972019-01-01300500110.1051/itmconf/20193005001itmconf_crimico2019_05001The method of array antenna constructive synthesis on the basis of neural network approachMishchenko SergeyShatskiy VitaliyLitvinov AlexeyEliseev DenisThe method to decision constructive synthesis of array antennas was conducted. The method usefull when antenna elements can be in discreste states (for example: active element, passive element, excluded item, active element with discrete nominal of output power e.t.c). The method is based on neural network approach. The structure of a neural network consist of a classifying neural network and several approximating neural networks is substantiated. Input signals correspond to phase centers of array antenna elements. Number of output signals in classifying part is equal to discrete status of antenna element. Each approximating part of network has one output signal wich correspond to continious meaning. Separate parts of network preliminary learning with error back propagation method. The genetic algorithm of neural network learning with limited number of training coefficients is proposed. Examples of solving problems of constructive synthesis, with different indicators of the quality of neural network training are given.https://www.itm-conferences.org/articles/itmconf/pdf/2019/07/itmconf_crimico2019_05001.pdf |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Mishchenko Sergey Shatskiy Vitaliy Litvinov Alexey Eliseev Denis |
spellingShingle |
Mishchenko Sergey Shatskiy Vitaliy Litvinov Alexey Eliseev Denis The method of array antenna constructive synthesis on the basis of neural network approach ITM Web of Conferences |
author_facet |
Mishchenko Sergey Shatskiy Vitaliy Litvinov Alexey Eliseev Denis |
author_sort |
Mishchenko Sergey |
title |
The method of array antenna constructive synthesis on the basis of neural network approach |
title_short |
The method of array antenna constructive synthesis on the basis of neural network approach |
title_full |
The method of array antenna constructive synthesis on the basis of neural network approach |
title_fullStr |
The method of array antenna constructive synthesis on the basis of neural network approach |
title_full_unstemmed |
The method of array antenna constructive synthesis on the basis of neural network approach |
title_sort |
method of array antenna constructive synthesis on the basis of neural network approach |
publisher |
EDP Sciences |
series |
ITM Web of Conferences |
issn |
2271-2097 |
publishDate |
2019-01-01 |
description |
The method to decision constructive synthesis of array antennas was conducted. The method usefull when antenna elements can be in discreste states (for example: active element, passive element, excluded item, active element with discrete nominal of output power e.t.c). The method is based on neural network approach. The structure of a neural network consist of a classifying neural network and several approximating neural networks is substantiated. Input signals correspond to phase centers of array antenna elements. Number of output signals in classifying part is equal to discrete status of antenna element. Each approximating part of network has one output signal wich correspond to continious meaning. Separate parts of network preliminary learning with error back propagation method. The genetic algorithm of neural network learning with limited number of training coefficients is proposed. Examples of solving problems of constructive synthesis, with different indicators of the quality of neural network training are given. |
url |
https://www.itm-conferences.org/articles/itmconf/pdf/2019/07/itmconf_crimico2019_05001.pdf |
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