DocumentCode
2049050
Title
On the learning of min-max fuzzy systems
Author
Tan, Shaohua ; Zhang, Li ; Vandewalle, Joos
Author_Institution
Dept. of Electr. Eng., Nat. Univ. of Singapore, Singapore
Volume
3
fYear
1997
fDate
1-5 Jul 1997
Firstpage
1581
Abstract
This paper develops a methodology for learning fuzzy systems that contain min-max operations. It is shown that with the defuzzification properly defined, a min-max fuzzy system can have a transparent structure that is both universally approximating and easy for a learning scheme to be developed. A specific learning scheme based on multi-scale residue extraction is then presented
Keywords
fuzzy set theory; fuzzy systems; inference mechanisms; learning (artificial intelligence); minimax techniques; defuzzification; inference mechanism; learning scheme; min-max fuzzy systems; multiscale residue extraction; structure transparency; Analytical models; Approximation algorithms; Approximation methods; Function approximation; Fuzzy logic; Fuzzy sets; Fuzzy systems; Marine vehicles; Quantization;
fLanguage
English
Publisher
ieee
Conference_Titel
Fuzzy Systems, 1997., Proceedings of the Sixth IEEE International Conference on
Conference_Location
Barcelona
Print_ISBN
0-7803-3796-4
Type
conf
DOI
10.1109/FUZZY.1997.619777
Filename
619777
Link To Document