Processing of missing data in a fuzzy system

Abstract

The more information is processed in a system the more likely is that some input values are missing. The paper describes (i) a method for managing the incomplete input data in a Mamdani fuzzy system and (ii) discusses the influence of inference interpretation on an efficiency of the fuzzy system operating on incomplete data. Two fuzzy models of missing information are discussed theoretically and then presented on an example of iris data set. Various interpretations of fuzzy rules and various types of membership function are examined in order to find a solution of fuzzy system that is more robust to missing data. © 2008 Springer-Verlag Berlin Heidelberg.