Itzamá López-Yáñez

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In this work a new Bidirectional Associative Memory model, surpassing every other past and current model, is presented. This new model is based on Alpha–Beta associative memories, from whom it inherits its name. The main and most important characteristic of Alpha–Beta bidirectional associative memories is that they exhibit perfect recall of all patterns in(More)
Bidirectional Associative Memories (BAM) based on Kosko’s model are implemented through iterative algorithms and present stability problems. Also, these models along with other models based on different methods, have not been able to perfectly recall all trained patterns. In this paper we present an English-Spanish / Spanish-English translator based on a(More)
In this paper, an algorithm which enables Alpha-Beta associative memories to learn and recall color images is presented. The latter is done even though these memories were originally designed by Yáñez-Márquez [1] to work only with binary patterns. Also, an experimental study on the proposed algorithm is presented, showing the efficiency of the new memories.
Pattern recognition and classification are two of the key topics in computer science. In this paper a novel method for the task of pattern classification is presented. The proposed method combines a hybrid associative classifier (Clasificador Híbrido Asociativo con Traslación, CHAT, in Spanish), a coding technique for output patterns called one-hot vector(More)
The paper describes a novel associative model for the forecasting of time series in petroleum engineering. The model is based on the Gamma classifier, which is inspired on the Alpha-Beta associative memories, taking the alpha and beta operators as basis for the gamma operator. The objective is to reproduce and predict future oil production in different(More)