DocumentCode
3148530
Title
Defuzzification, structure transparency, and fuzzy system learning
Author
Tan, Shaohua ; Vandewalle, Joos
Author_Institution
Dept. of Electr. Eng., Nat. Univ. of Singapore, Singapore
Volume
1
fYear
1996
fDate
8-11 Sep 1996
Firstpage
470
Abstract
The issue of defuzzification is explored in the context of fuzzy system structure and learning for nonlinear system modeling. It is revealed that the best-known defuzzification methods may not necessarily result in transparent fuzzy system structures that are universally approximate and yet suitable for developing effective learning algorithms for modeling. This paper then presents a simple defuzzification method that leads to transparent fuzzy system structures based on the min-max operations
Keywords
fuzzy logic; fuzzy systems; inference mechanisms; learning (artificial intelligence); minimax techniques; modelling; nonlinear systems; defuzzification; fuzzy system learning; inference mechanism; min-max operations; nonlinear system modeling; structure transparency; Adaptive algorithm; Convergence; Fuzzy sets; Fuzzy systems; Inference algorithms; Linear systems; Nonlinear systems;
fLanguage
English
Publisher
ieee
Conference_Titel
Fuzzy Systems, 1996., Proceedings of the Fifth IEEE International Conference on
Conference_Location
New Orleans, LA
Print_ISBN
0-7803-3645-3
Type
conf
DOI
10.1109/FUZZY.1996.551786
Filename
551786
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