Theta-fuzzy Associative Memories (theta-fams)

dc.creatorEsmi
dc.creatorEstevao; Sussner
dc.creatorPeter; Bustince
dc.creatorHumberto; Fernandez
dc.creatorJavier
dc.date2015-APR
dc.date2016-06-07T13:33:18Z
dc.date2016-06-07T13:33:18Z
dc.date.accessioned2018-03-29T01:49:04Z
dc.date.available2018-03-29T01:49:04Z
dc.identifier
dc.identifierTheta-fuzzy Associative Memories (theta-fams). Ieee-inst Electrical Electronics Engineers Inc, v. 23, p. 313-326 APR-2015.
dc.identifier1063-6706
dc.identifierWOS:000352279600006
dc.identifier10.1109/TFUZZ.2014.2312131
dc.identifierhttp://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6767099
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/243671
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1307369
dc.descriptionMost fuzzy associative memories (FAMs) in the literature correspond to neural networks with a single layer of weights that distributively contains the information on associations to be stored. The main applications of these types of associative memory can be found in fuzzy rule-based systems. In contrast, T-fuzzy associative memories (T-FAMs) represent parametrized fuzzy neural networks with a hidden layer and these FAM models extend (dual) S-FAMs and SM-FAMs based on fuzzy subsethood and similarity measures. In this paper, we provide theoretical results concerning the storage capacity and error correction capability of T-FAMs. In addition, we introduce a training algorithm for T-FAMs and we compare the error rates produced by T-FAMs and some well-known classifiers in some benchmark classification problems that are available on the internet. Finally, we apply T-FAMs to a problem of vision-based self-localization in mobile robotics.
dc.description23
dc.description2
dc.description
dc.description313
dc.description326
dc.descriptionFoundation for Research Support of the State of Sao Paulo [2009/16284-2, 2011/10014-3]
dc.description
dc.description
dc.description
dc.languageen
dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.publisher
dc.publisherPISCATAWAY
dc.relationIEEE TRANSACTIONS ON FUZZY SYSTEMS
dc.rightsfechado
dc.sourceWOS
dc.subjectDi-subsethood Measures
dc.subjectSet-theory
dc.subjectMathematical Morphologies
dc.subjectImplication Operators
dc.subjectSimilarity Measures
dc.subjectInclusion Measure
dc.subjectNeural-networks
dc.subjectGray-scale
dc.subjectClassification
dc.subjectConstruction
dc.titleTheta-fuzzy Associative Memories (theta-fams)
dc.typeArtículos de revistas


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