Abstract
Actually associative memories have demonstrated to be useful in pattern processing field. Hopfield model is an autoassociative memory that has problems in the recalling phase; one of them is the time of convergence or non convergence in certain cases with patterns bad recovered. In this paper, a new algorithm for the Hopfield associative memory eliminates iteration processes reducing time computing and uncertainty on pattern recalling. This algorithm is implemented using a corrective vector which is computed using the Hopfield memory. The corrective vector adjusts misclassifications in output recalled patterns. Results show a good performance of the proposed algorithm, providing an alternative tool for the pattern recognition field.
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Carbajal Hernández, J.J., Sánchez Fernández, L.P. (2011). Efficient Pattern Recalling Using a Non Iterative Hopfield Associative Memory. In: Batyrshin, I., Sidorov, G. (eds) Advances in Soft Computing. MICAI 2011. Lecture Notes in Computer Science(), vol 7095. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25330-0_46
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DOI: https://doi.org/10.1007/978-3-642-25330-0_46
Publisher Name: Springer, Berlin, Heidelberg
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