DocumentCode :
443986
Title :
Statistical interval-valued fuzzy systems via linear regression
Author :
Qiu, Yu ; Zhang, Yan-Qing ; Zhao, Yichuan
Author_Institution :
Dept. of Comput. Sci., Georgia State Univ., Atlanta, GA, USA
Volume :
1
fYear :
2005
fDate :
25-27 July 2005
Firstpage :
229
Abstract :
In recent years, the type-2 fuzzy sets theory has been used to model and minimize the effects of uncertainties in rule-base fuzzy logic system. In order to make the type-2 fuzzy logic system reasonable and reliable, a new simple statistical linear method to decide interval-valued fuzzy membership functions and a new probability type reduce reasoning method for the interval-valued fuzzy logic system are proposed in this paper. An example of statistical interval-valued FLS is performed and results show that the developed method is more accurate to design a fuzzy logic system than type-1 method and computation is efficient.
Keywords :
fuzzy control; fuzzy reasoning; fuzzy set theory; fuzzy systems; knowledge based systems; probability; regression analysis; type theory; uncertainty handling; fuzzy control; fuzzy reasoning; linear regression; probability type reduce reasoning method; rule-base fuzzy logic system; statistical interval-valued fuzzy logic systems; type-2 fuzzy logic system; type-2 fuzzy sets theory; uncertainty handling; Computer science; Fuzzy control; Fuzzy logic; Fuzzy reasoning; Fuzzy set theory; Fuzzy sets; Fuzzy systems; Linear regression; Probability; Uncertainty; Interval-valued fuzzy logic; fuzzy control; statistical interval-valued fuzzy reasoning; type-2 fuzzy logic;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Granular Computing, 2005 IEEE International Conference on
Print_ISBN :
0-7803-9017-2
Type :
conf
DOI :
10.1109/GRC.2005.1547273
Filename :
1547273
Link To Document :
بازگشت