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Authors
Boris Martinez; Jesus Fernandez; Erick Marichal; Francisco Herrera

Digital Object Identifier (DOI)
10.3182/20070213-3-CU-2913.00044

Page Numbers:
256-261

Index Terms
fuzzy modelling,Takagi-Sugeno models,on-line clustering,on-line identification

Abstract
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.

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