NDLI: Kernel method for constructing fuzzy inference system

Content Provider	IEEE Xplore Digital Library
Author	Liangkuan Zhu Guangfu Ma Zhong Shi
Copyright Year	2006
Description	Author affiliation: Dept. of Control Sci. & Eng., Harbin Inst. of Technol. (Liangkuan Zhu; Guangfu Ma)
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
Starting Page	4
Ending Page	509
File Size	1366792
Page Count	506
File Format	PDF
ISBN	0780393953
DOI	10.1109/ISSCAA.2006.1627674
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-01-19
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fuzzy logic Support vector machines Fuzzy sets Fuses Input variables Buildings Training data Machine learning Kernel Fuzzy systems
Content Type	Text
Resource Type	Article

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