Iranian Journal of Electrical and Electronic Engineering

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.