Name
Abstract | ||
|---|---|---|
Cascade process, such as wastewater treatment plant, includes many nonlinear sub-systems and many variables. When the number of sub-systems is big, the input-output relation in the first block and the last block cannot represent the whole process. In this paper we use two techniques to overcome the above problem. Firstly we propose a new neural model: hierarchical neural networks to identify the cascade process; then we use serial structural mechanism model based on the physical equations to connect with neural model. A stable learning algorithm and theoretical analysis are given. Finally, this method is used to model a wastewater treatment plant. Real operational data of wastewater treatment plant is applied to illustrate the modeling approach. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1142/S012906571000219X | INTERNATIONAL JOURNAL OF NEURAL SYSTEMS |
Keywords | Field | DocType |
Modeling, cascade process, hierarchical neural networks, wastewater treatment process | Nonlinear system,Process modeling,Control engineering,Artificial intelligence,Cascade,Engineering,Artificial neural network | Journal |
Volume | Issue | ISSN |
20 | 1 | 0129-0657 |
Citations | PageRank | References |
3 | 0.44 | 13 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qiumei Cong | 1 | 3 | 0.44 |
| Wen Yu | 2 | 283 | 22.70 |
| Tianyou Chai | 3 | 2014 | 175.55 |