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 |
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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