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.
| Original language | English |
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| Pages | 555-560 |
| Number of pages | 6 |
| Publication status | Published - 1996 Jan 1 |
| Event | Proceedings of the 1996 IEEE International Conference on Neural Networks, ICNN. Part 1 (of 4) - Washington, DC, USA Duration: 1996 Jun 3 → 1996 Jun 6 |
Other
| Other | Proceedings of the 1996 IEEE International Conference on Neural Networks, ICNN. Part 1 (of 4) |
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| City | Washington, DC, USA |
| Period | 96/6/3 → 96/6/6 |
ASJC Scopus subject areas
- Software