### 抄録

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.

元の言語 | English |
---|---|

ページ（範囲） | 2049-2057 |

ページ数 | 9 |

ジャーナル | Pattern Recognition |

巻 | 37 |

発行部数 | 10 |

DOI | |

出版物ステータス | Published - 2004 10 |

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### ASJC Scopus subject areas

- Computer Vision and Pattern Recognition
- Signal Processing
- Electrical and Electronic Engineering

### これを引用

*Pattern Recognition*,

*37*(10), 2049-2057. https://doi.org/10.1016/j.patcog.2004.04.003

**Adaptive fuzzy inference neural network.** / Iyatomi, Hitoshi; Hagiwara, Masafumi.

研究成果: Article

*Pattern Recognition*, 巻. 37, 番号 10, pp. 2049-2057. https://doi.org/10.1016/j.patcog.2004.04.003

}

TY - JOUR

T1 - Adaptive fuzzy inference neural network

AU - Iyatomi, Hitoshi

AU - Hagiwara, Masafumi

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

UR - http://www.scopus.com/inward/record.url?scp=4544268143&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=4544268143&partnerID=8YFLogxK

U2 - 10.1016/j.patcog.2004.04.003

DO - 10.1016/j.patcog.2004.04.003

M3 - Article

AN - SCOPUS:4544268143

VL - 37

SP - 2049

EP - 2057

JO - Pattern Recognition

JF - Pattern Recognition

SN - 0031-3203

IS - 10

ER -