DocumentCode
1641934
Title
Neural networks, qualitative-fuzzy logic and granular adaptive systems
Author
Lin, T.Y.
Author_Institution
Dept. of Math. & Comput. Sci., San Jose State Univ., CA, USA
Volume
1
fYear
2002
fDate
6/24/1905 12:00:00 AM
Firstpage
566
Lastpage
571
Abstract
Though traditional neural networks and fuzzy logic are powerful universal approximators, however without some refinements, they may not, in general, be good approximators for adaptive systems. By extending fuzzy sets to qualitative fuzzy sets, fuzzy logic may become universal approximators for adaptive systems. Similar considerations can be extended to neural networks
Keywords
adaptive systems; approximation theory; common-sense reasoning; fuzzy logic; fuzzy set theory; neural nets; adaptive systems; fuzzy logic; fuzzy sets; granular adaptive systems; neural networks; qualitative-fuzzy logic; universal approximators; Adaptive systems; Data structures; Earth; Equations; Fuzzy logic; Fuzzy sets; Geology; Mathematics; Neural networks; Physics;
fLanguage
English
Publisher
ieee
Conference_Titel
Fuzzy Systems, 2002. FUZZ-IEEE'02. Proceedings of the 2002 IEEE International Conference on
Conference_Location
Honolulu, HI
Print_ISBN
0-7803-7280-8
Type
conf
DOI
10.1109/FUZZ.2002.1005054
Filename
1005054
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