Adaptive cascade enhancement broad learning system combined with stacked correlation information autoencoder for soft sensor modeling of industrial process
•A new feature extraction method which introduces the correlation coefficient and the dominant variable into the stacked autoencoder has been developed.•An adaptive algorithm which effectively determines the number of nodes of the broad learning system has been developed.•Two examples are used to de...
Uloženo v:
| Vydáno v: | Computers & chemical engineering Ročník 177; s. 108324 |
|---|---|
| Hlavní autoři: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier Ltd
01.09.2023
|
| Témata: | |
| ISSN: | 0098-1354 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Shrnutí: | •A new feature extraction method which introduces the correlation coefficient and the dominant variable into the stacked autoencoder has been developed.•An adaptive algorithm which effectively determines the number of nodes of the broad learning system has been developed.•Two examples are used to demonstrate the effectiveness of the proposed method.
In the actual soft sensor project, the complex industrial process leads to a large number of monitoring variables that lead to obvious problems of high dimension and data redundancy. To solve these problems, an adaptive cascade enhancement broad learning system combined with stacked correlation information autoencoder, referred to as SCIAE-ACEBLS, is proposed in this study. The latter is based on the broad learning system (BLS) and it includes two parts: feature node and enhancement node. In the feature node part, the correlation coefficient and the dominant variable are introduced into the stacked autoencoder (SAE), and all the hidden nodes are used as feature nodes. Both the correlation coefficient and the dominant variable are introduced to ensure that the information of feature nodes not only include the relevant information of the dominant variable in the auxiliary variables, but also in the dominant variable, and thus all the information related to dominant variables can be effectively used. In the enhancement node part, an adaptive cascade enhancement node algorithm is used to reduce the redundancy of effective information and solve the problems of node information redundancy and node number uncertainty caused by the randomness of the node parameters in BLS. Finally, two industrial examples show that the proposed model is effective and outperforms existing methods. |
|---|---|
| ISSN: | 0098-1354 |
| DOI: | 10.1016/j.compchemeng.2023.108324 |