Structure identification of fuzzy model
Fuzzy Sets and Systems
Identification of non-linear system structure and parameters using regime decomposition
Automatica (Journal of IFAC)
Fuzzy Modeling for Control
Subtractive clustering based modeling of job sequencing with parametric search
Fuzzy Sets and Systems - Data analysis
Uncertainty prediction for tool wear condition using type-2 TSK fuzzy approach
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
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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.