AN INDUCTIVE LEARNING PROCEDURE TO IDENTIFY FUZZY-SYSTEMS

In many real cases, a system may not be described by a functional input-output description and the only way to describe it is through a finite set of fuzzy rules. Even in some cases where exact identification is possible, looking for that rule description may be useful for the sake of ease and effectiveness. The automatic identification of a system through the analysis of an input-output data set is the aim of this work. In order to identify a system from raw data, obtained in repeated instances of its own system, the frequency of appearance of some kind of patterns may be particularly interesting. Nevertheless, when the base domain is fuzzy and the data can be crisp, statistical methods are not very appropriate and it is necessary to study a new model for frequencies on fuzzy domains. The proposed model for identifying systems is based on this definition.