Chaotic associative memory for successive learning using internal patterns

Norihiro Kawasaki, Yuko Osana, Masafumi Hagiwara

研究成果: Conference article査読

12 被引用数 (Scopus)

抄録

In this paper, we propose a chaotic associative memory for successive learning (CAMSL) using internal patterns. In the CAMSL, the learning process and the recall process are not divided. When an unstored pattern is given to the network, the CAMSL can learn the pattern successively. The CAMSL distinguishes an unstored pattern from the stored patterns. When a stored pattern is given, the CAMSL recalls the pattern. When an unstored pattern is given, the CAMSL changes the internal pattern for the input pattern by chaos and presents the other pattern candidates. When the CAMSL cannot recall the desired pattern, it learns the input pattern as an unstored pattern. We carried out a series of computer simulations and confirmed the effectiveness of the CAMSL.

本文言語English
ページ(範囲)2521-2526
ページ数6
ジャーナルProceedings of the IEEE International Conference on Systems, Man and Cybernetics
4
出版ステータスPublished - 2000 12月 1
イベント2000 IEEE International Conference on Systems, Man and Cybernetics - Nashville, TN, USA
継続期間: 2000 10月 82000 10月 11

ASJC Scopus subject areas

  • 制御およびシステム工学
  • ハードウェアとアーキテクチャ

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