Autogeneration of fuzzy rules and membership functions for fuzzy modelling using rough set theory

The rough set theory can represent a degree of consistency between condition and decision attributes of data pairs which do not have linguistic information. By using this ability, a measure called occupancy degree is defined, which can represent the degree of consistency between premise and consequent variables in fuzzy rules describing given experimental data pairs. A method is also proposed by which the projected data is partitioned on the input space, and an optimal fuzzy rule table and membership functions of input and output variables are found from data without preliminary linguistic information. The validity of the proposed method is examined by modelling data pairs which are randomly generated from a fuzzy system.