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Please use this identifier to cite or link to this item: https://libeldoc.bsuir.by/handle/123456789/54447
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dc.contributor.authorJiran Guo-
dc.coverage.spatialМинскen_US
dc.date.accessioned2024-03-01T07:37:33Z-
dc.date.available2024-03-01T07:37:33Z-
dc.date.issued2023-
dc.identifier.citationJiran Guo. Model identification of wood drying and shrinkage processes / Jiran Guo // Pattern Recognition and Information Processing (PRIP'2023) = Распознавание образов и обработка информации (2023) : Proceedings of the 16th International Conference, October 17–19, 2023, Minsk, Belarus / United Institute of Informatics Problems of the National Academy of Sciences of Belarus. – Minsk, 2023. – P. 279–282.en_US
dc.identifier.urihttps://libeldoc.bsuir.by/handle/123456789/54447-
dc.description.abstractIn the drying process of wood, the controlling quantities are temperature and humidity, which in turn lead to changes in moisture content and further lead to drying of wood to produce dry shrinkage force. In this paper, the ARMA model is used to identify the process of temperature-moisture- moisture content, and then the control model of moisture content and shrinkage force is developed on the basis of the ARMA model.The results show that the combination of the ARMA model and the BP neural network can form a good control model for the drying shrinkage force, which can provide a feasible basis for the application of the ARMA model and the BP neural network in the drying shrinkage force of wood.en_US
dc.language.isoenen_US
dc.publisherBSUen_US
dc.subjectматериалы конференцийen_US
dc.subjectwood dryingen_US
dc.subjectARMA modelen_US
dc.subjectBP networken_US
dc.titleModel identification of wood drying and shrinkage processesen_US
dc.typeArticleen_US
Appears in Collections:Pattern Recognition and Information Processing (PRIP'2023) = Распознавание образов и обработка информации (2023)

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