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A New Model of BAM: Alpha-Beta Bidirectional Associative Memories

机译:BAM的新模型:Alpha-Beta双向联想记忆

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Most models of Bidirectional associative memories intend to achieve that all trained pattern correspond to stable states; however, this has not been possible. Also, none of the former models has been able to recall all the trained patterns. In this work we introduce a new model of bidirectional associative memory which is not iterative and has no stability problems. It is based on the Alpha-Beta associative memories. This model allows, besides correct recall of noisy patterns, perfect recall of all trained patterns, with no ambiguity and no conditions. An example of fingerprint recognition is presented.
机译:双向关联存储器的大多数模型都旨在实现所有训练好的模式都对应于稳定状态。但是,这是不可能的。同样,以前的模型都无法回忆起所有训练过的模式。在这项工作中,我们引入了一种双向联想记忆的新模型,该模型不是迭代的并且没有稳定性问题。它基于Alpha-Beta关联记忆。除了正确召回嘈杂的模式外,该模型还可以完美召回所有经过训练的模式,而不会产生歧义和条件。给出了指纹识别的示例。

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