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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Main Authors: Mishchenko Sergey, Shatskiy Vitaliy, Litvinov Alexey, Eliseev Denis
Format: Article
Language:English
Published: EDP Sciences 2019-01-01
Series:ITM Web of Conferences
Online Access:https://www.itm-conferences.org/articles/itmconf/pdf/2019/07/itmconf_crimico2019_05001.pdf
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spelling 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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