Abstract
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. © Springer-Verlag Berlin Heidelberg 2006.
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CITATION STYLE
Acevedo-Mosqueda, M. E., Yáñez-Márquez, C., & López-Yáñez, I. (2006). A new model of BAM: Alpha-beta bidirectional associative memories. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4263 LNCS, pp. 286–295). Springer Verlag. https://doi.org/10.1007/11902140_32
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