Abstract
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.
| Original language | English |
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| Pages | 1080-1085 |
| Number of pages | 6 |
| Publication status | Published - 1994 Dec 1 |
| Event | Proceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7) - Orlando, FL, USA Duration: 1994 Jun 27 → 1994 Jun 29 |
Other
| Other | Proceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7) |
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| City | Orlando, FL, USA |
| Period | 94/6/27 → 94/6/29 |
ASJC Scopus subject areas
- Software