Adaptive fuzzy inference neural network

Hitoshi Iyatomi, Masafumi Hagiwara

研究成果: Article

40 引用 (Scopus)

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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Fuzzy inference
Neural networks
Parameter estimation
Fuzzy systems
Identification (control systems)

ASJC Scopus subject areas

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

これを引用

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

：: Pattern Recognition, 巻 37, 番号 10, 10.2004, p. 2049-2057.

研究成果: Article

Iyatomi, H & Hagiwara, M 2004, 'Adaptive fuzzy inference neural network', Pattern Recognition, 巻. 37, 番号 10, pp. 2049-2057. https://doi.org/10.1016/j.patcog.2004.04.003
Iyatomi H, Hagiwara M. Adaptive fuzzy inference neural network. Pattern Recognition. 2004 10;37(10):2049-2057. https://doi.org/10.1016/j.patcog.2004.04.003
Iyatomi, Hitoshi ; Hagiwara, Masafumi. / Adaptive fuzzy inference neural network. ：: Pattern Recognition. 2004 ; 巻 37, 番号 10. pp. 2049-2057.
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