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Improved GART neural network model for pattern classification and rule extraction with application to power systems
journal contribution
posted on 2011-12-01, 00:00 authored by K Yap, Chee Peng LimChee Peng Lim, M AuGeneralized 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.
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Journal
IEEE transactions on neural networksVolume
22Issue
12Pagination
2310 - 2323Publisher
IEEELocation
Piscataway, N. J.ISSN
1045-9227eISSN
1941-0093Language
engPublication classification
C1.1 Refereed article in a scholarly journalUsage metrics
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fuzzy inference systemsgeneralized adaptive resonance theorypattern classificationrule extractionScience & TechnologyTechnologyComputer Science, Artificial IntelligenceComputer Science, Hardware & ArchitectureComputer Science, Theory & MethodsEngineering, Electrical & ElectronicComputer ScienceEngineeringTRANSFORMER FAILURE DIAGNOSISDISSOLVED-GAS ANALYSISFAULT-DETECTIONFUZZY ARTMAPMULTIDIMENSIONAL MAPSOIL ANALYSISARCHITECTUREGENERATIONALGORITHMBOXES
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