This paper describes the application of an identification algorithm clustering type Gustafson-Kessel nonlinear dynamical system. From input-output data the algorithm generates fuzzy models of Takagi-Sugeno. This type of modeling is applied to a non-linear numerical model. The non-linear input / output model of the system is decomposed in several described by membership functions and fuzzy rule-based local linear systems. The results are presented and prospects for future work.

boilier identification using Takagi Sugeno.pdf

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