A neuro-fuzzy classifier and its applications
Chuen–Tsai Sun, Jyh‐Shing Roger Jang · 2002
The authors propose a general fuzzy classification scheme with learning ability using an adaptive network. System parameters, such as the membership functions defined for each feature and the parameterized t-norms used to combine conjunctive conditions, are calibrated with backpropagation. To explain this approach, the concept of adaptive networks is introduced and a supervised learning procedure based on a gradient descent algorithm is derived to update the parameters in an adaptive network. The proposed architecture is applied to two problems: two-spiral classification and Iris categorization. From the experimental results, it is concluded that the adaptively adjusted classifier performs well on an Iris classification problem. The results are discussed from the viewpoint of feature selection.>