Recursive Fuzzy Model Identification

D. Dovžan and I. Škrjanc (Slovenia)


Recursive Fuzzy Clustering, Recursive Fuzzy Identification, Clustering, Online Recursive Identification.


In this paper we propose an approach to on-line Takagi Sugeno fuzzy model identification. It combines a recursive fuzzy c-means algorithm and recursive least squares. First the recursive clustering algorithm is derived. This is used to recursively update the positions of the centers and variance of the clusters. Then the recursive least squares are applied to obtain the sub-models parameters. The proposed algorithm can be used in a number of fields, including adaptive nonlinear control, model predictive control, fault detection, diagnostic and robotics.

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