Neural associative memory for intelligent information processing

Motonobu Hattori, Masafumi Hagiwara

研究成果: Paper査読

1 被引用数 (Scopus)

抄録

In this paper, first we derive a novel relaxation method for the system of linear inequalities and apply it to the learning for associative memories. Since the proposed intersection learning can guarantee the recall of all training data, it can greatly enlarge the storage capacity of associative memories. In addition, it requires much less weights renewal times than the conventional methods. We also propose a multimodule associative memory which can be learned by the intersection learning algorithm. The proposed associative memory can deal with many-to-many associations and it is applied to a knowledge processing task. Computer simulation results show the effectiveness of the proposed learning algorithm and associative memory.

本文言語English
ページ377-386
ページ数10
出版ステータスPublished - 1998 12月 1
外部発表はい
イベントProceedings of the 1998 2nd International Conference on knowledge-Based Intelligent Electronic Systems (KES '98) - Adelaide, Aust
継続期間: 1998 4月 211998 4月 23

Other

OtherProceedings of the 1998 2nd International Conference on knowledge-Based Intelligent Electronic Systems (KES '98)
CityAdelaide, Aust
Period98/4/2198/4/23

ASJC Scopus subject areas

  • コンピュータ サイエンス(全般)

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