Intersection learning for bidirectional associative memory

Motonobu Hattori; Masafumi Hagiwara

Intersection learning for bidirectional associative memory

Motonobu Hattori
, Masafumi Hagiwara

情報工学科

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
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月 3 → 1996 6月 6

Other

Other	Proceedings of the 1996 IEEE International Conference on Neural Networks, ICNN. Part 1 (of 4)
City	Washington, DC, USA
Period	96/6/3 → 96/6/6

ASJC Scopus subject areas

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引用スタイル

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title = "Intersection learning for bidirectional associative memory",

abstract = "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.",

author = "Motonobu Hattori and Masafumi Hagiwara",

year = "1996",

language = "English",

pages = "555--560",

note = "Proceedings of the 1996 IEEE International Conference on Neural Networks, ICNN. Part 1 (of 4) ; Conference date: 03-06-1996 Through 06-06-1996",

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TY - CONF

T1 - Intersection learning for bidirectional associative memory

AU - Hattori, Motonobu

AU - Hagiwara, Masafumi

PY - 1996

Y1 - 1996

N2 - 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.

AB - 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.

UR - https://www.scopus.com/pages/publications/0029728312

UR - https://www.scopus.com/pages/publications/0029728312#tab=citedBy

M3 - Paper

AN - SCOPUS:0029728312

SP - 555

EP - 560

T2 - Proceedings of the 1996 IEEE International Conference on Neural Networks, ICNN. Part 1 (of 4)

Y2 - 3 June 1996 through 6 June 1996

ER -

Intersection learning for bidirectional associative memory

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