Dealing with uncertainty is one of the most critical problems in complicated
pattern recognition subjects. In this paper, we modify the structure of a useful Unsupervised
Fuzzy Neural Network (UFNN) of Kwan and Cai, and compose a new FNN with 6 types of
fuzzy neurons and its associated self organizing supervised learning algorithm. This
improved five-layer feed forward Supervised Fuzzy Neural Network (SFNN) is used for
classification and identification of shifted and distorted training patterns. It is generally
useful for those flexible patterns which are not certainly identifiable upon their features. To
show the identification capability of our proposed network, we used fingerprint, as the most
flexible and varied pattern. After feature extraction of different shapes of fingerprints, the
pattern of these features, “feature-map”, is applied to the network. The network first
fuzzifies the pattern and then computes its similarities to all of the learned pattern classes.
The network eventually selects the learned pattern of highest similarity and returns its
specific class as a non fuzzy output. To test our FNN, we applied the standard (NIST
database) and our databases (with 176×224 dimensions). The feature-maps of these
fingerprints contain two types of minutiae and three types of singular points, each of them
is represented by 22×28 pixels, which is less than real size and suitable for real time
applications. The feature maps are applied to the FNN as training patterns. Upon its setting
parameters, the network discriminates 3 to 7 subclasses for each main classes assigned to
one of the subjects.