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.
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Acevedo-Mosqueda, M.E., Yáñez-Márquez, C., López-Yáñez, I. (2006). Complexity of Alpha-Beta Bidirectional Associative Memories. In: Gelbukh, A., Reyes-Garcia, C.A. (eds) MICAI 2006: Advances in Artificial Intelligence. MICAI 2006. Lecture Notes in Computer Science(), vol 4293. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11925231_34
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DOI: https://doi.org/10.1007/11925231_34
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