Robust identification of Takagi-Sugeno-Kang fuzzy models using regularization

The identification of fuzzy models can sometimes be a difficult problem, often characterized by lack of data in some regions, collinearities and other data deficiencies, or a sub-optimal choice of model structure. Regularization is suggested as a general method for improving the robustness of standard parameter identification algorithms leading to more accurate and well-behaved fuzzy models. The properties of the method are related to the bias/variance tradeoff, and illustrated with a semi-realistic simulation example.

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