A new preprocessing approach to preparation of binary patterns for fam neural networks

The patterns which are presented to a Fuzzy ARTmap network should be preprocessed in such a way that the data are of appropriate clearance. In order to decrease the degree of similarity between the normalized binary patterns, a new method is proposed in which the binary patterns are converted to fuzzy (analogue) patterns. This is done by weighting and normalizing the values, determining the binary pattern, according to the number of ones which surround that value. By using this method, the noise in the input patterns is rejected and therefore noisy patterns are not considered as new training patterns. This increases the efficiency of the proposed method.