The behavior of forgetting learning in bidirectional associative memory
Neural Computation
Analysis on Bidirectional Associative Memories with Multiplicative Weight Noise
Neural Information Processing
Lagrange programming neural networks for compressive sampling
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
Recurrent networks for compressive sampling
Neurocomputing
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In this paper, a bidirectional associative memory (BAM) model with second-order connections, namely second-order bidirectional associative memory (SOBAM), is first reviewed. The stability and statistical properties of the SOBAM are then examined. We use an example to illustrate that the stability of the SOBAM is not guaranteed. For this result, we cannot use the conventional energy approach to estimate its memory capacity. Thus, we develop the statistical dynamics of the SOBAM. Given that a small number of errors appear in the initial input, the dynamics shows how the number of errors varies during recall. We use the dynamics to estimate the memory capacity, the attraction basin, and the number of errors in the retrieved items. Extension of the results to higher-order bidirectional associative memories is also discussed