DocumentCode :
1872915
Title :
Kernel method for constructing fuzzy inference system
Author :
Zhu, Liangkuan ; Ma, Guangfu ; Shi, Zhong
Author_Institution :
Dept. of Control Sci. & Eng., Harbin Inst. of Technol.
fYear :
2006
fDate :
19-21 Jan. 2006
Lastpage :
509
Abstract :
This paper exhibits the connection between fuzzy inference system and kernel machines, and proposes a support vector learning approach to construct fuzzy inference system so that it can have good generalization ability in a high dimensional feature space. It is showed that the two seemingly unrelated research areas, fuzzy inference systems and kernel machines, are closely related. Under some minor constrains, the equivalence of the two seemingly quite distinct models is proved. The designed fuzzy inference system can be represented as a decision function consisting of series expansion of modified fuzzy basis functions (MFBFs), and this also makes itself to be interpretable. The approach preserves advantages of both the statistical learning framework and the fuzzy inference system. The performance of the proposed approach is illustrated by an example of nonlinear function regression
Keywords :
fuzzy set theory; inference mechanisms; support vector machines; decision function; dimensional feature space; fuzzy inference system; kernel machines; modified fuzzy basis functions; nonlinear function regression; statistical learning framework; support vector learning; Buildings; Fuses; Fuzzy logic; Fuzzy sets; Fuzzy systems; Input variables; Kernel; Machine learning; Support vector machines; Training data;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Systems and Control in Aerospace and Astronautics, 2006. ISSCAA 2006. 1st International Symposium on
Conference_Location :
Harbin
Print_ISBN :
0-7803-9395-3
Type :
conf
DOI :
10.1109/ISSCAA.2006.1627674
Filename :
1627674
Link To Document :
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