Competitive and hybrid neuro-fuzzy models for supervised classification

Neuro-fuzzy systems are often very complex and may require long training times. In the context of supervised classification, we propose a competitive and a hybrid model based on fuzzy basis function networks. These models are fast to train and still hold very good generalization performances. Experimental results on the classification of handwritten digits are presented