An adaptive rule extraction with the fuzzy self-organizing map and a comparison with other methods

Abstract

For automatic rule extraction from a set of input-output data examples, decision tree generating methods such as ID3 and fuzzy ID3 play a major role. These methods, however, are difficult to apply when there is a tendency for the examples to change dynamically. This paper presents a new method for adaptive rule extraction with the fuzzy self-organizing map and the results of simulations in order to present its effectiveness by a comparison with other methods such as RBF (radial basis functions) and GA (genetic algorithms). We obtained the result that our method is superior to other methods for automatic and adaptive rule extraction.