RULE SPECIALIZATION IN NETWORKS OF FUZZY BASIS FUNCTIONS

The structure identification of adaptive fuzzy logic systems, realized as networks of Fuzzy Basis Functions (FBF's) and trained on numerical data, is studied for a handwritten character recognition problem. An FBF network with fewer rules than classes to be discriminated is unabl e to recognize some classes, while, when the number of rules is increa sed up to the number of classes to be discriminated, a sharp increase in the performance is observed. Experimental results point out that th e behavior of the FBF network is closer to that of a competitive model showing a strong specialization of the fuzzy rules.