Application of self-organizing network and MLP for fuzzy rule extraction

A multi-stage algorithm for classification and fuzzy rule extraction from data based on a self-organizing network and a two-layer perceptron network is proposed. Self-organizing techniques are applied to find data prototypes, which are subsequently used to initialize the perceptron network and to produce membership functions from the learned mapping. Fuzzy rules created by the algorithm provide linguistic description of the relationship encoded in data.