Adaptive fuzzy inference neural network

被引：36
|
作者：
Iyatomi, H [1 ]
Hagiwara, M [1 ]
机构：
[1] Keio Univ, Dept Informat & Comp Sci, Yokohama, Kanagawa 2238522, Japan
来源：
PATTERN RECOGNITION | 2004年 / 37卷 / 10期
关键词：
neural network; fuzzy inference; machine learning; fuzzy modeling and rule extraction;
D O I：
10.1016/j.patcog.2004.04.003
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
An adaptive fuzzy inference neural network (AFINN) is proposed in this paper. It has self-construction ability, parameter estimation ability and rule extraction ability. The structure of AFINN is formed by the following four phases: (1) initial rule creation, (2) selection of important input elements, (3) identification of the network structure and (4) parameter estimation using LMS (least-mean square) algorithm. When the number of input dimension is large, the conventional fuzzy systems often cannot handle the task correctly because the degree of each rule becomes too small. AFINN solves such a problem by modification of the learning and inference algorithm. (C) 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
引用
收藏
页码：2049 / 2057
页数：9
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