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Improved GART neural network model for pattern classification and rule extraction with application to power systems

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dc.citedby43
dc.contributor.authorYap K.S.en_US
dc.contributor.authorLim C.P.en_US
dc.contributor.authorAu M.T.en_US
dc.contributor.authorid24448864400en_US
dc.contributor.authorid55666579300en_US
dc.contributor.authorid9742020600en_US
dc.date.accessioned2023-12-29T07:47:13Z
dc.date.available2023-12-29T07:47:13Z
dc.date.issued2011
dc.description.abstractGeneralized adaptive resonance theory (GART) is a neural network model that is capable of online learning and is effective in tackling pattern classification tasks. In this paper, we propose an improved GART model (IGART), and demonstrate its applicability to power systems. IGART enhances the dynamics of GART in several aspects, which include the use of the Laplacian likelihood function, a new vigilance function, a new match-tracking mechanism, an ordering algorithm for determining the sequence of training data, and a rule extraction capability to elicit if-then rules from the network. To assess the effectiveness of IGART and to compare its performances with those from other methods, three datasets that are related to power systems are employed. The experimental results demonstrate the usefulness of IGART with the rule extraction capability in undertaking classification problems in power systems engineering. � 2006 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo6069866
dc.identifier.doi10.1109/TNN.2011.2173502
dc.identifier.epage2323
dc.identifier.issue12 PART 2
dc.identifier.scopus2-s2.0-83655167009
dc.identifier.spage2310
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-83655167009&doi=10.1109%2fTNN.2011.2173502&partnerID=40&md5=b9f90b52abbb9bfe3954a9abec6e01ca
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/30380
dc.identifier.volume22
dc.pagecount13
dc.sourceScopus
dc.sourcetitleIEEE Transactions on Neural Networks
dc.subjectFuzzy inference systems
dc.subjectgeneralized adaptive resonance theory
dc.subjectpattern classification
dc.subjectrule extraction
dc.subjectData Mining
dc.subjectDatabases, Factual
dc.subjectElectric Power Supplies
dc.subjectElectricity
dc.subjectFeedback
dc.subjectModels, Theoretical
dc.subjectNeural Networks (Computer)
dc.subjectPattern Recognition, Automated
dc.subjectFuzzy inference
dc.subjectOnline systems
dc.subjectPattern recognition
dc.subjectPower transmission
dc.subjectAdaptive resonance theory
dc.subjectData sets
dc.subjectFuzzy inference systems
dc.subjectIf-then rules
dc.subjectLaplacians
dc.subjectLikelihood functions
dc.subjectNeural network model
dc.subjectOnline learning
dc.subjectOrdering algorithms
dc.subjectRule extraction
dc.subjectTraining data
dc.subjectarticle
dc.subjectartificial neural network
dc.subjectautomated pattern recognition
dc.subjectdata mining
dc.subjectelectricity
dc.subjectfactual database
dc.subjectfeedback system
dc.subjectmethodology
dc.subjectpower supply
dc.subjecttheoretical model
dc.subjectNeural networks
dc.titleImproved GART neural network model for pattern classification and rule extraction with application to power systemsen_US
dc.typeArticleen_US
dspace.entity.typePublication
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