An Improved Fuzzy Neural Network for Solving Uncertainty in Pattern Classification and Identification

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.