An Improved Fuzzy Neural Network for Solving Uncertainty in Pattern Classification and Identification - M. Hariri; S. B. Shokouhi; N. Mozayani (Iranian Journal of Electrical and Electronic Engineering, 2008)
Dealing with uncertainty is one of the most critical problems in complicatedpattern recognition subjects. In this paper, we modify the structure of a useful UnsupervisedFuzzy Neural Network (UFNN) of Kwan and Cai, and compose a new FNN with 6 types offuzzy neurons and its associated self organizing supervised learning algorithm. Thisimproved five-layer feed forward Supervised Fuzzy Neural Network (SFNN) is used forclassification and identification of shifted and distorted training patterns. It is generallyuseful for those flexible patterns which are not certainly identifiable upon their features. Toshow the identification capability of our proposed network, we used fingerprint, as the mostflexible and varied pattern. After feature extraction of different shapes of fingerprints, thepattern of these features, “feature-map”, is applied to the network. The network firstfuzzifies 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 itsspecific class as a non fuzzy output. To test our FNN, we applied the standard (NISTdatabase) and our databases (with 176×224 dimensions). The feature-maps of thesefingerprints contain two types of minutiae and three types of singular points, each of themis represented by 22×28 pixels, which is less than real size and suitable for real timeapplications. The feature maps are applied to the FNN as training patterns. Upon its settingparameters, the network discriminates 3 to 7 subclasses for each main classes assigned toone of the subjects.