Title :
Neural networks, qualitative-fuzzy logic and granular adaptive systems
Author_Institution :
Dept. of Math. & Comput. Sci., San Jose State Univ., CA, USA
fDate :
6/24/1905 12:00:00 AM
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;
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
DOI :
10.1109/FUZZ.2002.1005054
