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.
|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 3 → 1996 6 6
|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