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.