Input selection in data-driven fuzzy modeling

An iterative backward selection method for determination of relevant input variables in data-driven fuzzy modeling is presented. The method utilizes parameters of the Takagi-Sugeno model as a factor to determine the significance of input variables. As a result, it is less computationally intensive than most of the existing methods for input variable selection.