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• W2125200922 abstract "Morphological associative memories are among several types of morphological neural network models which have been proposed over the course of the last few years. A neural network is called morphological if one of the fundamental operations of mathematical morphology, a dilation or an erosion, is performed at each node. These operations can be expressed as a or a in the mathematical theory of minimax algebra. This paper employs set theory to generalize the operations max product and min product as used in binary autoassociative morphological memory (AMM) models. Replacing the original operations by new operations fuzzy product and fuzzy product in this setting yields a AMM with crisp input patterns and output patterns. A thresholding procedure can be applied to obtain crisp output patterns. This new approach significantly improves the error correction capability of binary autoassociative morphological memories." @default.
• W2125200922 created "2016-06-24" @default.
• W2125200922 creator A5078829305 @default.
• W2125200922 date "2004-03-02" @default.
• W2125200922 modified "2023-10-04" @default.
• W2125200922 title "A fuzzy autoassociative morphological memory" @default.
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• W2125200922 doi "https://doi.org/10.1109/ijcnn.2003.1223366" @default.
• W2125200922 hasPublicationYear "2004" @default.
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