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A Data-Driven Approach of Takagi-Sugeno Fuzzy Control of Unknown Nonlinear Systems
2020
Applied Sciences
A novel approach to build a Takagi-Sugeno (T-S) fuzzy model of an unknown nonlinear system from experimental data is presented in the paper. The neuro-fuzzy models or, more specifically, fuzzy basis function networks (FBFNs) are trained from input–output data to approximate the nonlinear systems for which analytical mathematical models are not available. Then, the T-S fuzzy models are derived from the direct linearization of the neuro-fuzzy models. The operating points for linearization are
doi:10.3390/app11010062
fatcat:y4viby7qgvbo3el5kv2gyumdcy