Noise immunization of a neural fuzzy intelligent recognition system by the use of feature and rule extraction technique

The performance of a neural fuzzy intelligent recognition system(NFIRS) which recognizes varied levels of noise-corrupted characters was investigated in this paper. The number of regions in the universe of discourse of the input space was first arbitrarily selected. Then, the centers of these regions were self-organized by feeding the system with 256-pixel alphabet and algebraic training samples to the Kohonen competitive learning network. Based on the reallocated centers, we tried several combinations of varied rule-region product in order to generate a smaller set of fuzzy rules. We fixed the number of features to simplify and simulation and to isolate the effect of rule extraction. Simulation results showed an NFIRS that uses a set of thirty six sampling data set as the training input will generate a set of thirty six if ... then ... fuzzy rules which can be used to recognize a corrupted testing data set without sacrifice the rate of recognition under varied conditions.