Abstract
A hybrid approach for fuzzy system design based on clustering and a kind of neurofuzzy networks is proposed. An unsupervised clustering technique is firstly used to determine the number of if-then fuzzy rules and generate an initial fuzzy rule base from the given input-output data. Then, a class of neurofuzzy networks is constructed and its weights are tuned so that the obtained fuzzy rule base has a high accuracy. Finally, two examples of function approximation problems are given to illustrate the effectiveness of the proposed approach.
| Original language | English |
|---|---|
| Pages (from-to) | 299-303 |
| Number of pages | 5 |
| Journal | Journal of Systems Engineering and Electronics |
| Volume | 15 |
| Issue number | 3 |
| State | Published - Sep 2004 |
Keywords
- Clustering
- Fuzzy rule base
- Fuzzy systems design
- Neurofuzzy networks