Abstract
Most models of Bidirectional Associative Memories intend to achieve that all trained patterns 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. A new model which appeared recently, called Alpha-Beta Bidirectional Associative Memory (BAM), recalls 100% of the trained patterns, without error. Also, the model is non iterative and has no stability problems. In this work the analysis of time and space complexity of the Alpha-Beta BAM is presented. © Springer-Verlag Berlin Heidelberg 2006.
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CITATION STYLE
Acevedo-Mosqueda, M. E., Yáñez-Márquez, C., & López-Yáñez, I. (2006). Complexity of Alpha-Beta bidirectional associative memories. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4293 LNAI, pp. 357–366). Springer Verlag. https://doi.org/10.1007/11925231_34
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