A general framework for fuzzy morphological associative memories

dc.creatorValle, ME
dc.creatorSussner, P
dc.date2008
dc.dateAPR 1
dc.date2014-11-14T18:07:53Z
dc.date2015-11-26T16:07:46Z
dc.date2014-11-14T18:07:53Z
dc.date2015-11-26T16:07:46Z
dc.date.accessioned2018-03-28T22:56:26Z
dc.date.available2018-03-28T22:56:26Z
dc.identifierFuzzy Sets And Systems. Elsevier Science Bv, v. 159, n. 7, n. 747, n. 768, 2008.
dc.identifier0165-0114
dc.identifier1872-6801
dc.identifierWOS:000254477900001
dc.identifier10.1016/j.fss.2007.10.010
dc.identifierhttp://www.repositorio.unicamp.br/jspui/handle/REPOSIP/75852
dc.identifierhttp://www.repositorio.unicamp.br/handle/REPOSIP/75852
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/75852
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1266217
dc.descriptionFuzzy associative memories (FAMs) can be used as a powerful tool for implementing fuzzy rule-based systems. The insight that FAMs are closely related to mathematical morphology (MM) has recently led to the development of new fuzzy morphological associative memories (FMAMs), in particular implicative fuzzy associative memories (IFAMs). As the name FMAM indicates, these models belong to the class of fuzzy morphological neural networks (FMNNs). Thus, each node of an FMAM performs an elementary operation of fuzzy MM. Clarifying several misconceptions about FMAMs that have recently appeared in the literature, we provide a general framework for FMAMs within the class of FMNN. We show that many well-known FAM models fit within this framework and can therefore be classified as FMAMs. Moreover, we employ certain concepts of duality that are defined in the general theory of MM in order to derive a large class of strategies for learning and recall in FMAMs. (C) 2007 Elsevier B.V. All rights reserved.
dc.description159
dc.description7
dc.description747
dc.description768
dc.languageen
dc.publisherElsevier Science Bv
dc.publisherAmsterdam
dc.publisherHolanda
dc.relationFuzzy Sets And Systems
dc.relationFuzzy Sets Syst.
dc.rightsfechado
dc.rightshttp://www.elsevier.com/about/open-access/open-access-policies/article-posting-policy
dc.sourceWeb of Science
dc.subjectfuzzy inference systems
dc.subjectfuzzy associative memories
dc.subjectfuzzy mathematical morphology
dc.subjectfuzzy morphological associative memories
dc.subjectfuzzy learning by adjunction
dc.subjectNeural-networks
dc.subjectMathematical Morphologies
dc.subjectGray-scale
dc.subjectComplete Lattices
dc.subjectRecognition
dc.subjectSystems
dc.subjectBinary
dc.titleA general framework for fuzzy morphological associative memories
dc.typeArtículos de revistas


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