Sensitivity analysis for type-1 and type-2 TSK fuzzy models

Authors:
Qun Ren;Luc Baron;Marek Balazinski
Affiliations:
Mechanical engineering department, École Polytechnique de Montréal, Montréal, Québec, Canada;Mechanical engineering department, École Polytechnique de Montréal, Montréal, Québec, Canada;Mechanical engineering department, École Polytechnique de Montréal, Montréal, Québec, Canada
Venue:
MOAS'07 Proceedings of the 18th conference on Proceedings of the 18th IASTED International Conference: modelling and simulation
Year:
2007

Citing 4
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Abstract

In this paper, subtractive clustering method is combined with least squares estimation algorithms to pre-identify a type-1 Takagi-Sugeno-Kang (TSK) fuzzy model from input/output data. Then the type-2 fuzzy theory is used to expand the type-1 model to a type-2 model. A sensitivity analysis is used to ascertain how a type-1 TSK model output depends upon the pre-initialized parameters and determine how a type-2 TSK model output depends upon spread percentages of cluster centers and consequent parameters. By using sensitivity analysis, we can check the quality of TSK models, and characterize the uncertainty associated with the TSK fuzzy models.