FUZZY MODELLING OF NONLINEAR SYSTEMS USING ON LINE CLUSTERING METHODS

This paper presents an approach for online Takagi-Sugeno fuzzy models generation, which can be applied for nonlinear systems identification. The algorithm combines a proposed on-line clustering technique with least squares methods. Both the structure and parameters of the fuzzy system are updated on line. The new clustering method for the structure identification can divide input-output data into different groups (rules) using on line data. After the rules are determined, the consequent parameters are tuned by using least squares estimators.