Title :
Input selection in data-driven fuzzy modeling
Author :
Gaweda, Adam E. ; Zurada, Jacek M. ; Setiono, Rudy
Author_Institution :
Dept. of Electr. & Comput. Eng., Louisville Univ., KY, USA
fDate :
6/23/1905 12:00:00 AM
Abstract :
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
Keywords :
computational complexity; fuzzy set theory; modelling; Takagi-Sugeno model; computational complexity; computational intensiveness; data-driven fuzzy modeling; input selection; iterative backward selection method; relevant input variable determination; Computational efficiency; Data engineering; Data mining; Fuzzy sets; Fuzzy systems; Hydrogen; Input variables; Iterative algorithms; Iterative methods; Testing;
Conference_Titel :
Fuzzy Systems, 2001. The 10th IEEE International Conference on
Conference_Location :
Melbourne, Vic.
Print_ISBN :
0-7803-7293-X
DOI :
10.1109/FUZZ.2001.1008885
