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.
|出版ステータス||Published - 1994|
|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 27 → 1994 6月 29
|Other||Proceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7)|
|City||Orlando, FL, USA|
|Period||94/6/27 → 94/6/29|
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