The basic concept of membership function, fuzziness measures and fuzzy geometry of image subsets are also discussed in this regard. The next part discusses on a neuro fuzzy computing. This includes, first of all, the necessity for artificial neural network classifier, the relevance of neuro-fuzzy approach and the different ways of integrating artificial neural network and fuzzy logic. Some examples of application specific merits from the point of pattern recognition, pixel classification under noisy environment and feature evaluation are then described. Methods of linguistic rule generation and querying for missing features are also explained.
Feature evaluation includes both selection and extraction under supervised and unsupervised modes.
The methodology basically involves formulation of different fuzzy feature evaluation indices followed by their optimization in various connectionist frameworks. Here the basic principle involves automatic placement of an appropriate number of hyperplanes for constituting class boundaries in order to result in an optimal classification rate. The concepts of variable length genetic algorithms and other new operators are used. Its relation with Baye's classifier, analogy with MLP and superiority over k-NN classifier are stated. Finally, the merits of integrating genetic algorithms with artificial neural networks and fuzzy logic for building a stronger computational paradigm are addressed.