| DC Field | Value | Language |
|---|
| dc.contributor.author | Jiran Guo | - |
| dc.coverage.spatial | Минск | en_US |
| dc.date.accessioned | 2024-03-01T07:37:33Z | - |
| dc.date.available | 2024-03-01T07:37:33Z | - |
| dc.date.issued | 2023 | - |
| dc.identifier.citation | Jiran 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.uri | https://libeldoc.bsuir.by/handle/123456789/54447 | - |
| dc.description.abstract | In 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.iso | en | en_US |
| dc.publisher | BSU | en_US |
| dc.subject | материалы конференций | en_US |
| dc.subject | wood drying | en_US |
| dc.subject | ARMA model | en_US |
| dc.subject | BP network | en_US |
| dc.title | Model identification of wood drying and shrinkage processes | en_US |
| dc.type | Article | en_US |
| Appears in Collections: | Pattern Recognition and Information Processing (PRIP'2023) = Распознавание образов и обработка информации (2023)
|
