We present a neuro-fttzzy architecture for function approximation based on supervised learning. The learning algorithm is able to determine the structure and the parameters of a fuzzy system. The approach is an extension to our already published NEFCON and NEFCLASS models which are used for control
Neuro-fuzzy relational systems for nonlinear approximation and prediction
✍ Scribed by Rafał Scherer
- Publisher
- Elsevier Science
- Year
- 2009
- Tongue
- English
- Weight
- 464 KB
- Volume
- 71
- Category
- Article
- ISSN
- 0362-546X
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