Name
Abstract | ||
|---|---|---|
It was examined how different fuzzy modelling approaches, such as neuro-fuzzy, fuzzy clustering and linguistic equation methods, apply to the modelling of a rotary dryer. Because rotary drying, one of the oldest process in industry, is a highly nonlinear, strongly interactive multivariable process, its modelling is a demanding task. Its mathematical model, consisting of partial differential equations with several experimental parameters, is very complex and cumbersome. Therefore, the data-driven model is attractive, especially because many experimental observations and operating experience exist. The paper describes the fuzzy modelling approaches applied to the modelling of a rotary dryer. The applicability of different approaches has been evaluated by simulations, with the data collected from a pilot plant rotary dryer. The performance was estimated by an error index root means squared method and by comparing the modelling results with the results achieved by a linear regression model and a neural network model. The results show that neuro-fuzzy, fuzzy clustering and linguistic equation methods apply well, and no big differences can be detected between the methods. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1080/00207720310001640304 | INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE |
Keywords | Field | DocType |
fuzzy clustering,indexation,partial differential equation,neuro fuzzy,root mean square,linear regression model,data collection,neural network model,mathematical model | Fuzzy clustering,Square (algebra),Nonlinear system,Data-driven,Multivariable calculus,Control theory,Control engineering,Fuzzy modelling,Partial differential equation,Mathematics,Linear regression | Journal |
Volume | Issue | ISSN |
34 | 14-15 | 0020-7721 |
Citations | PageRank | References |
1 | 0.40 | 3 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| L. Yliniemi | 1 | 2 | 0.85 |
| J. Koskinen | 2 | 1 | 0.74 |
| Kauko Leiviskä | 3 | 38 | 6.66 |