Improving Premise Structure in Evolving Takagi-Sugeno Neuro-Fuzzy Classifiers
Résumé
We present in this paper a new method for the design of evolving neuro-fuzzy classifiers. The presented approach is based on a first-order Takagi-Sugeno neuro-fuzzy model. We propose a modification on the premise structure in this model and we provide the necessary learning formulas, with no problem-dependent parameters. We demonstrate by the experimental results the positive effect of this modification on the overall classification performance
Domaines
Apprentissage [cs.LG]| Origine | Fichiers produits par l'(les) auteur(s) |
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