Name
Abstract | ||
|---|---|---|
A simple and effective fuzzy modelling approach is presented in this paper. A three-layer hierarchical clustering neural network
is developed to build fuzzy rule-based models from numerical data. Differing from existing clustering-based methods, in this
approach the structure identification of the fuzzy model is implemented on the basis of a class of sub-clusters created by
a self-organising network instead of on raw data. By combined use of unsupervised and supervised learning, both structure
identification and parameter optimisation of the fuzzy model can be carried out automatically. The simulation results show
that the proposed method can provide good model structure for fuzzy modelling and has high computing efficiency. |
Year | DOI | Venue |
|---|---|---|
2000 | 10.1007/s005210070034 | Neural Computing and Applications |
Keywords | DocType | Volume |
fuzzy clustering,fuzzy modelling,neu- ral fuzzy systems,non-linear system identification | Journal | 9 |
Issue | ISSN | Citations |
1 | 1433-3058 | 2 |
PageRank | References | Authors |
0.64 | 2 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Min-you Chen | 1 | 274 | 22.18 |
| Derek A. Linkens | 2 | 215 | 25.36 |