Name
Abstract | ||
|---|---|---|
The model of propylene distillation helps improve the quality of propylene products. This paper proposes a methodology of constructing the model of propylene distillation based on the neural network technique. The strategy of adjusting the neural network-based model of propylene distillation with rough sets is proposed. A numerical example of the neural network-based model for actual propylene distillation is provided. A comparison is made between the predicted results from the model and the actual results, which validates the effectiveness of the model of propylene distillation. © 2007 IEEE. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/ISIC.2007.4450924 | 22nd IEEE International Symposium on Intelligent Control, ISIC 2007. Part of IEEE Multi-conference on Systems and Control |
Keywords | Field | DocType |
distillation,chemical industry,rough set theory,neural nets,neural network,rough set | Chemical industry,Computer science,Control engineering,Rough set,Distillation,Artificial neural network | Conference |
Volume | Issue | ISSN |
null | null | null |
Citations | PageRank | References |
1 | 0.35 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jingui Lu | 1 | 31 | 4.22 |
| Ning Fang | 2 | 1 | 0.35 |
| Jinguo Lin | 3 | 1 | 0.35 |
| Fengxin Chen | 4 | 7 | 2.73 |
| Guanghui Chen | 5 | 1 | 0.35 |