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A hybrid ART-GRNN online learning neural network with a ε-insensitive loss function
journal contribution
posted on 2008-09-01, 00:00 authored by K Yap, Chee Peng LimChee Peng Lim, I AbidinIn this brief, a new neural network model called generalized adaptive resonance theory (GART) is introduced. GART is a hybrid model that comprises a modified Gaussian adaptive resonance theory (MGA) and the generalized regression neural network (GRNN). It is an enhanced version of the GRNN, which preserves the online learning properties of adaptive resonance theory (ART). A series of empirical studies to assess the effectiveness of GART in classification, regression, and time series prediction tasks is conducted. The results demonstrate that GART is able to produce good performances as compared with those of other methods, including the online sequential extreme learning machine (OSELM) and sequential learning radial basis function (RBF) neural network models.
History
Journal
IEEE transactions on neural networksVolume
19Issue
9Pagination
1641 - 1646Publisher
IEEELocation
Piscataway, N. J.ISSN
1045-9227eISSN
1941-0093Language
engPublication classification
C1.1 Refereed article in a scholarly journalCopyright notice
2008, IEEEUsage metrics
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No categories selectedKeywords
adaptive resonance theory (ART)bayesian theoremgeneralized regression neural network (GRNN)online sequential extreme learning machineScience & TechnologyTechnologyComputer Science, Artificial IntelligenceComputer Science, Hardware & ArchitectureComputer Science, Theory & MethodsEngineering, Electrical & ElectronicComputer ScienceEngineeringFUZZY ARTMAPNOISY MAPPINGSREGRESSIONALGORITHMAPPROXIMATION
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