Adaptive fuzzy inference neural network

Hitoshi Iyatomi; Masafumi Hagiwara

doi:10.1016/j.patcog.2004.04.003

Adaptive fuzzy inference neural network

Hitoshi Iyatomi, Masafumi Hagiwara

Department of Information and Computer Science

Research output: Contribution to journal › Article › peer-review

43 Citations (Scopus)

Abstract

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.

Original language	English
Pages (from-to)	2049-2057
Number of pages	9
Journal	Pattern Recognition
Volume	37
Issue number	10
DOIs	https://doi.org/10.1016/j.patcog.2004.04.003
Publication status	Published - 2004 Oct

Keywords

Fuzzy inference
Fuzzy modeling and rule extraction
Machine learning
Neural network

ASJC Scopus subject areas

Software
Signal Processing
Computer Vision and Pattern Recognition
Artificial Intelligence

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10.1016/j.patcog.2004.04.003

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title = "Adaptive fuzzy inference neural network",

abstract = "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.",

keywords = "Fuzzy inference, Fuzzy modeling and rule extraction, Machine learning, Neural network",

author = "Hitoshi Iyatomi and Masafumi Hagiwara",

note = "Funding Information: This work is partly supported by the grants-in-aid from Ministry of Education, Culture, Sports, Science and Technology (C-12650394). ",

year = "2004",

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AU - Iyatomi, Hitoshi

AU - Hagiwara, Masafumi

N1 - Funding Information: This work is partly supported by the grants-in-aid from Ministry of Education, Culture, Sports, Science and Technology (C-12650394).

PY - 2004/10

Y1 - 2004/10

N2 - 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.

AB - 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.

KW - Fuzzy inference

KW - Fuzzy modeling and rule extraction

KW - Machine learning

KW - Neural network

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Adaptive fuzzy inference neural network

Abstract

Keywords

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