Adaptive fuzzy inference neural network

Hitoshi Iyatomi, Masafumi Hagiwara

研究成果: Article査読

42 被引用数 (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.

本文言語English
ページ(範囲)2049-2057
ページ数9
ジャーナルPattern Recognition
37
10
DOI
出版ステータスPublished - 2004 10月

ASJC Scopus subject areas

  • ソフトウェア
  • 信号処理
  • コンピュータ ビジョンおよびパターン認識
  • 人工知能

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