Digital transformation of machine technology for onion harvesting using the theory of artificial neural networks
The problem of forecasting the qualitative indicators of onion harvesters was solved using the methodologies of the system analysis and synthesis, physical modeling, based on the theory of artificial neural networks. Analysis of the mathematical model of the working process of onion harvesting machi...
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Federal Agricultural Research Center of the North-East named N.V. Rudnitsky
2019-02-01
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Online Access: | https://www.agronauka-sv.ru/jour/article/view/305 |
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doaj-f74cb14e964d45539322c699608b48b72021-08-30T08:55:13ZrusFederal Agricultural Research Center of the North-East named N.V. RudnitskyАграрная наука Евро-Северо-Востока2072-90812500-13962019-02-01201849110.30766/2072-9081.2019.20.1.84-91303Digital transformation of machine technology for onion harvesting using the theory of artificial neural networksA. V. Sibiriev0A. S. Dorokhov1A. G. Aksenov2Federal Scientific Agroengineering Center VIMFederal Scientific Agroengineering Center VIMFederal Scientific Agroengineering Center VIMThe problem of forecasting the qualitative indicators of onion harvesters was solved using the methodologies of the system analysis and synthesis, physical modeling, based on the theory of artificial neural networks. Analysis of the mathematical model of the working process of onion harvesting machine showed that the increase in the quality indicators of onion harvesting can be ensured by the optimal ratio of internal unregulated parameters of separate executive devices. A change in the process parameters of mechanical means for onion harvesting within design limits does not ensure keeping to agrotechnical requirements. This neural network model for the assessment of quality indicators of functioning elements of the machine for harvesting onion set allows to predict the quality performance indicators on the basis of a large number of external impacts X, affecting the harvesting process. The theory of artificial neural networks allows to describe the technological working process of the machine for harvesting onion set, its individual functioning elements, to predict and evaluate the quality performance indicators both of separate executive devices and the entire machine.https://www.agronauka-sv.ru/jour/article/view/305modelingdynamic systemmathematical modelqualitative indicators |
collection |
DOAJ |
language |
Russian |
format |
Article |
sources |
DOAJ |
author |
A. V. Sibiriev A. S. Dorokhov A. G. Aksenov |
spellingShingle |
A. V. Sibiriev A. S. Dorokhov A. G. Aksenov Digital transformation of machine technology for onion harvesting using the theory of artificial neural networks Аграрная наука Евро-Северо-Востока modeling dynamic system mathematical model qualitative indicators |
author_facet |
A. V. Sibiriev A. S. Dorokhov A. G. Aksenov |
author_sort |
A. V. Sibiriev |
title |
Digital transformation of machine technology for onion harvesting using the theory of artificial neural networks |
title_short |
Digital transformation of machine technology for onion harvesting using the theory of artificial neural networks |
title_full |
Digital transformation of machine technology for onion harvesting using the theory of artificial neural networks |
title_fullStr |
Digital transformation of machine technology for onion harvesting using the theory of artificial neural networks |
title_full_unstemmed |
Digital transformation of machine technology for onion harvesting using the theory of artificial neural networks |
title_sort |
digital transformation of machine technology for onion harvesting using the theory of artificial neural networks |
publisher |
Federal Agricultural Research Center of the North-East named N.V. Rudnitsky |
series |
Аграрная наука Евро-Северо-Востока |
issn |
2072-9081 2500-1396 |
publishDate |
2019-02-01 |
description |
The problem of forecasting the qualitative indicators of onion harvesters was solved using the methodologies of the system analysis and synthesis, physical modeling, based on the theory of artificial neural networks. Analysis of the mathematical model of the working process of onion harvesting machine showed that the increase in the quality indicators of onion harvesting can be ensured by the optimal ratio of internal unregulated parameters of separate executive devices. A change in the process parameters of mechanical means for onion harvesting within design limits does not ensure keeping to agrotechnical requirements. This neural network model for the assessment of quality indicators of functioning elements of the machine for harvesting onion set allows to predict the quality performance indicators on the basis of a large number of external impacts X, affecting the harvesting process. The theory of artificial neural networks allows to describe the technological working process of the machine for harvesting onion set, its individual functioning elements, to predict and evaluate the quality performance indicators both of separate executive devices and the entire machine. |
topic |
modeling dynamic system mathematical model qualitative indicators |
url |
https://www.agronauka-sv.ru/jour/article/view/305 |
work_keys_str_mv |
AT avsibiriev digitaltransformationofmachinetechnologyforonionharvestingusingthetheoryofartificialneuralnetworks AT asdorokhov digitaltransformationofmachinetechnologyforonionharvestingusingthetheoryofartificialneuralnetworks AT agaksenov digitaltransformationofmachinetechnologyforonionharvestingusingthetheoryofartificialneuralnetworks |
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