A classifier using fuzzy rules extracted directly from numerical data

The authors consider the extraction of fuzzy rules directly from numerical data for pattern classification. The fuzzy rules with variable fuzzy regions are defined by activation hyperboxes which show the existence region for a class, and inhibition hyperboxes which inhibit the existence of data for that class. These rules are extracted from numerical data by recursively resolving overlaps between two classes. Then optimal input variables for the rules are determined using the number of extracted rules as a criterion. The method is compared with neural networks for a licence plate recognition system