Intersection learning for bidirectional associative memory

Motonobu Hattori, Masafumi Hagiwara

研究成果: Paper査読

4 被引用数 (Scopus)

抄録

In this paper, we propose Intersection Learning for Bidirectional Associative Memory (ILBAM). The proposed ILBAM is based on a novel relaxation method. A number of computer simulations show the following effectiveness of the proposed ILBAM: (1) It can guarantee the recall of all training pairs. (2) It requires much less weights renewal times than the conventional methods. (3) It becomes more effective in case there are many training pairs to be stored. (4) It is insensitive to the correlation of training pairs. (5) It contributes to the noise reduction effect of the BAM.

本文言語English
ページ555-560
ページ数6
出版ステータスPublished - 1996 1 1
イベントProceedings of the 1996 IEEE International Conference on Neural Networks, ICNN. Part 1 (of 4) - Washington, DC, USA
継続期間: 1996 6 31996 6 6

Other

OtherProceedings of the 1996 IEEE International Conference on Neural Networks, ICNN. Part 1 (of 4)
CityWashington, DC, USA
Period96/6/396/6/6

ASJC Scopus subject areas

  • ソフトウェア

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