New results of quick learning for bidirectional associative memory having high capacity

Motonobu Hattori, Masafumi Hagiwara, Masao Nakagawa

研究成果: Paper査読

5 被引用数 (Scopus)

抄録

Several important characteristics of Quick Learning for Bidirectional Associative Memory (QLBAM) are introduced. QLBAM uses two stages learning. In the first stage, the BAM is trained by Hebbian learning and then by Pseudo-Relaxation Learning Algorithm for BAM (PRLAB). The following features of the QLBAM are made clear: 1) it is insensitive to correlation of training pairs; 2) it is robust for noisy inputs; 3) the minimum absolute value of net inputs indexes a noise margin; 4) the memory capacity is greatly improved: the maximum capacity in our simulation is about 2.2N.

本文言語English
ページ1080-1085
ページ数6
出版ステータスPublished - 1994 12月 1
イベントProceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7) - Orlando, FL, USA
継続期間: 1994 6月 271994 6月 29

Other

OtherProceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7)
CityOrlando, FL, USA
Period94/6/2794/6/29

ASJC Scopus subject areas

  • ソフトウェア

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