DocumentCode
1711732
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
Volume
3
fYear
2001
fDate
6/23/1905 12:00:00 AM
Firstpage
1251
Lastpage
1254
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;
fLanguage
English
Publisher
ieee
Conference_Titel
Fuzzy Systems, 2001. The 10th IEEE International Conference on
Conference_Location
Melbourne, Vic.
Print_ISBN
0-7803-7293-X
Type
conf
DOI
10.1109/FUZZ.2001.1008885
Filename
1008885
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