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An Extended Method of SIRMs Connected Fuzzy Inference Method Using Kernel Method

Hirosato SEKI, Fuhito MIZUGUCHI, Satoshi WATANABE, Hiroaki ISHII, Masaharu MIZUMOTO

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Summary :

The single input rule modules connected fuzzy inference method (SIRMs method) by Yubazaki et al. can decrease the number of fuzzy rules drastically in comparison with the conventional fuzzy inference methods. Moreover, Seki et al. have proposed a functional-type SIRMs method which generalizes the consequent part of the SIRMs method to function. However, these SIRMs methods can not be applied to XOR (Exclusive OR). In this paper, we propose a "kernel-type SIRMs method" which uses the kernel trick to the SIRMs method, and show that this method can treat XOR. Further, a learning algorithm of the proposed SIRMs method is derived by using the steepest descent method, and compared with the one of conventional SIRMs method and kernel perceptron by applying to identification of nonlinear functions, medical diagnostic system and discriminant analysis of Iris data.

Publication
IEICE TRANSACTIONS on Fundamentals Vol.E92-A No.10 pp.2514-2521
Publication Date
2009/10/01
Publicized
Online ISSN
1745-1337
DOI
10.1587/transfun.E92.A.2514
Type of Manuscript
Special Section PAPER (Special Section on Nonlinear Theory and its Applications)
Category
Nonlinear Problems

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