Structural Learning of Fuzzy Rules from Noised Examples

Inductive learning algorithms try to obtain the knowledge of a system from a set of examples. One of the most diicult problems in machine learning consists in getting the structure of this knowledge. We propose an algorithm able to manage with fuzzy information and able to learn the structure of the rules that represent the system. The algorithm gives a reasonable small set of fuzzy rules that represent the original set of examples.