DocumentCode
2386476
Title
Learning method of fuzzy functional inference model
Author
Seki, Hirosato ; Mizumoto, Masaharu
Author_Institution
Dept. of Technol. Manage., Osaka Inst. of Technol., Osaka, Japan
fYear
2011
fDate
9-12 Oct. 2011
Firstpage
3524
Lastpage
3529
Abstract
Seki and Mizumoto have proposed a fuzzy functional inference method in which the consequent parts of the T-S inference method is generalized to fuzzy functions, However, it is noted that the adjustment of fuzzy functions is generally difficult. Therefore, in this paper, we propose a learning method of the fuzzy functional inference method by using the base of the fuzzy functions. Moreover, the fuzzy functional inference method by using the proposed learning method is shown to be superior to the conventional method by applying to a medical diagnosis as an example of application to real systems.
Keywords
fuzzy set theory; inference mechanisms; learning (artificial intelligence); medical computing; patient diagnosis; T-S inference method; consequent parts; fuzzy functional inference model; learning method; medical diagnosis; Bismuth; Diabetes; Fuzzy sets; Gravity; Learning systems; Medical diagnosis; Silicon; Fuzzy inference; base function; fuzzy function; fuzzy functional inference method; medical diagnosis;
fLanguage
English
Publisher
ieee
Conference_Titel
Systems, Man, and Cybernetics (SMC), 2011 IEEE International Conference on
Conference_Location
Anchorage, AK
ISSN
1062-922X
Print_ISBN
978-1-4577-0652-3
Type
conf
DOI
10.1109/ICSMC.2011.6084215
Filename
6084215
Link To Document