Robust Identi cation of Takagi-Sugeno-Kang Fuzzy Models using Regularization
نویسنده
چکیده
The identiication of fuzzy models can sometimes be a diicult problem, often characterized by lack of data in some regions, collinearities and other data deecien-cies, or a sub-optimal choice of model structure. Regu-larization is suggested as a general method for improving the robustness of standard parameter identiication algorithms leading to more accurate and well-behaved fuzzy models. The properties of the method are related to the bias/variance tradeoo, and illustrated with a semi-realistic simulation example.
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