- Tytuł:
- Data-driven online modelling for a UGI gasification process using modified lazy learning with a relevance vector machine
- Autorzy:
-
Liu, Shida
Ji, Honghai
Hou, Zhongsheng
Zuo, Jiashuo
Fan, Lingling - Powiązania:
- https://bibliotekanauki.pl/articles/1838200.pdf
- Data publikacji:
- 2021
- Wydawca:
- Uniwersytet Zielonogórski. Oficyna Wydawnicza
- Tematy:
-
data-driven modelling
UGI gasification process
relevance vector machine
modified lazy learning
modelowanie oparte na danych
proces gazyfikacji
maszyna wektorów istotnych - Opis:
- A modified lazy learning algorithm combined with a relevance vector machine (MLL-RVM) is presented to address a data-driven modelling problem for a gasification process inside a united gas improvement (UGI) gasifier. During the UGI gasification process, the measured online temperature of the produced crude gas is a crucial aspect. However, the gasification process complexities, especially severe changes in the temperature versus infrequent manipulation of the gasifier and the unknown noise in collected data, pose difficulties in dynamics process descriptions via conventional first principles. In the MLL-RVM, a novel weighted neighbour selection method is adopted based on the proposed dynamic cost functions. Moreover, the RVM is utilized in the implementation and design of the proposed online local modelling owing to its short test time and sparseness. Furthermore, the leave-one-out cross-validation technique is used for local model validation, by which the modelling performance is further improved. The MLL-RVM is applied to a series of real data collected from a pragmatic UGI gasifier, and its effectiveness is verified.
- Źródło:
-
International Journal of Applied Mathematics and Computer Science; 2021, 31, 2; 321-335
1641-876X
2083-8492 - Pojawia się w:
- International Journal of Applied Mathematics and Computer Science
- Dostawca treści:
- Biblioteka Nauki
Artykuł