Data-based extraction of unidimensional fuzzy sets for fuzzy rule generation

For complex problems of data-based fuzzy modelling the computing time plays an important role. Thus, reduction of the problem size by restricting the search to promising possibilities is justified. This paper presents a new method for extracting unidimensional fuzzy sets from measured data for a subsequent rule generation process. This method is motivated by four main points: 1) the projection of multidimensional data to unidimensional fuzzy sets considers the dependence between the input variables and the output variable without anticipating the rule generation process; 2) the user is not required to predefine the number of fuzzy sets and the number is changeable in a flexible manner for each variable without new computations; 3) the sum of membership values of one variable is one; and 4) the computing time does not increase more than linearly with the number of input variables.