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Type: Artigo de periódico
Title: Generalizing operations of binary autoassociative morphological memories using fuzzy set theory
Author: Sussner, P
Abstract: Morphological neural networks (MNNs) are a class of artificial neural networks whose operations can be expressed in the mathematical theory of minimax algebra. In a morphological neural net, the usual sum of weighted inputs is replaced by a maximum or minimum of weighted inputs (in this context, the weighting is performed by summing the weight and the input). We speak of a max product, a min product respectively. In recent years, a number of different MNN models and applications have emerged. The emphasis of this paper is on morphological associative memories (MAMs), in particular on binary autoassociative morphological memories (AMMs). We give a new set theoretic interpretation of recording and recall in binary AMMs and provide a generalization using fuzzy set theory.
Subject: morphological neural network
associative memory
morphological associative memory
image algebra
fuzzy set theory
Country: Holanda
Editor: Kluwer Academic Publ
Citation: Journal Of Mathematical Imaging And Vision. Kluwer Academic Publ, v. 19, n. 2, n. 81, n. 93, 2003.
Rights: fechado
Identifier DOI: 10.1023/A:1024721313295
Date Issue: 2003
Appears in Collections:Unicamp - Artigos e Outros Documentos

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