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Type: Artigo de periódico
Title: Implicative fuzzy associative memories
Author: Sussner, P
Valle, ME
Abstract: Associative neural memories are models of biological phenomena that allow for the storage of pattern associations and the retrieval of the desired output pattern upon presentation of a possibly noisy or incomplete version of an input pattern. In this paper, we introduce implicative fuzzy associative memories (IFAMs), a class of associative neural memories based on fuzzy set theory. An IFAM consists of a network of completely interconnected Pedrycz logic neurons with threshold whose connection weights are determined by the minimum of implications of presynaptic and postsynaptic activations. We present a series of results for autoassociative models including one pass convergence, unlimited storage capacity and tolerance with respect to eroded patterns. Finally, we present some results on fixed points and discuss the relationship between implicative fuzzy associative memories and morphological associative memories.
Subject: associative memories
fuzzy Hebbian learning
fuzzy neural networks
fuzzy relations
fuzzy systems
morphological associative memories
storage capacity
tolerance with respect to noise
Country: EUA
Editor: Ieee-inst Electrical Electronics Engineers Inc
Citation: Ieee Transactions On Fuzzy Systems. Ieee-inst Electrical Electronics Engineers Inc, v. 14, n. 6, n. 793, n. 807, 2006.
Rights: fechado
Identifier DOI: 10.1109/TFUZZ.2006.879968
Date Issue: 2006
Appears in Collections:Unicamp - Artigos e Outros Documentos

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