Name
Abstract | ||
|---|---|---|
The availability of an accurate model can facilitate tasks such as design or monitoring in chemical industries. A well-constructed model is invaluable but it can be costly to construct due to the expense in acquiring the labeled data. In the effort to reduce cost of building model, transfer of information using available sources has been considered. However, only the linear transfer has been considered for chemical processes in the reported literature. Moreover, the unlabeled data problem for transfer is not considered too. In this work, a new Gaussian process (GP) model based transfer learning approach for modeling is proposed. The aim is to leverage the statistical approach of the GP to transfer the knowledge from some available data of other processes for both labeled and unlabeled data. These data is used to provide information that is lacking to the process of interest. Case studies are presented to demonstrate the features of the proposed method. |
Year | Venue | Keywords |
|---|---|---|
2019 | 2019 12th Asian Control Conference (ASCC) | multisource data transfer learning,well-constructed model,linear transfer,chemical processes,unlabeled data problem,Gaussian process model based transfer learning approach,GP,labeled data,reducing cost,process modeling |
Field | DocType | ISSN |
Multi source data,Data mining,Chemical process,Computer science,Process modeling,Transfer of learning,Building model,Gaussian process,Labeled data | Conference | 2072-5639 |
ISBN | Citations | PageRank |
978-1-7281-0263-4 | 0 | 0.34 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lester Lik Teck Chan | 1 | 0 | 0.68 |
| Jung-hui Chen | 2 | 33 | 8.60 |