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 |
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ページ(範囲) | 2049-2057 |
ページ数 | 9 |
ジャーナル | Pattern Recognition |
巻 | 37 |
号 | 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