Name
Abstract | ||
|---|---|---|
Shown in this paper is a practical method of control using neural network and fuzzy control techniques, where a neural network estimates the target of fuzzy control. The neural network is used to estimate the transient state of a plant which has nonlinear processes such as refrigerating and filtering. The suitable control target pattern for fuzzy control is selected according to this estimation. This method is applied to control the tank level of a solvent dewaxing plant for: 1) changing the tank outflow rate smoothly, and 2) keeping the tank level stable. The results show that this system can control the tank level effectively in both steady state and transient state |
Year | DOI | Venue |
|---|---|---|
1996 | 10.1109/91.531776 | IEEE T. Fuzzy Systems |
Keywords | Field | DocType |
neural network,tank level stable,suitable control target pattern,level control,petroleum plant,process control,neurocontrollers,transient state estimation,neuro-fuzzy hybrid control system,transient state,hybrid neuro-fuzzy control,state estimation,tank level,nonlinear processes,tank level control,petroleum industry,dewaxing plant,fuzzy control technique,steady state,fuzzy control,practical method,tank outflow rate,neural nets,neural networks,fuzzy logic,refining,control systems,petroleum,neuro fuzzy | Neuro-fuzzy,Control theory,Filter (signal processing),Transient state,Process control,Steady state,Control system,Fuzzy control system,Artificial neural network,Mathematics | Journal |
Volume | Issue | ISSN |
4 | 3 | 1063-6706 |
Citations | PageRank | References |
7 | 0.79 | 3 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| T. Tani | 1 | 9 | 1.27 |
| S. Murakoshi | 2 | 7 | 0.79 |
| Motohide Umano | 3 | 183 | 28.91 |