ART-2及其改进方法综述-豆柴文库

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ART-2及其改进方法综述
Artificialneuralnetworks(ANNs)areatypeofmachinelearningmodelinspiredbythebiologicalstructureofthebrain.AmongvarioustypesofANNs,theAdaptiveResonanceTheory-2(ART-2)isapopularchoiceforpatternrecognitiontasksduetoitsabilitytoseparateandclassifyinputpatternsintodifferentcategories.Overtheyears,researchershaveproposedseveralimprovementstoenhancetheperformanceofART-2.Inthispaper,weprovideanoverviewofART-2anditsvariousenhancements,discussingtheiradvantages,limitations,andpotentialapplications.
ART-2wasfirstintroducedbyStephenGrossbergintheearly1980sasanextensionoftheoriginalAdaptiveResonanceTheory(ART)model.ThemainprinciplebehindART-2istheconceptofresonance,whichreferstotheprocessofmatchinganinputpatterntoaprototypeorcategory.ART-2incorporatesbothonlineandofflinelearningmodes,allowingittodynamicallyadjustitsrecognitioncapabilitiesbasedonnewincomingpatterns.
ThebasicarchitectureofART-2consistsoftwolayers-theinputlayerandtheoutputlayer.Theinputlayerreceivestheinputpatterns,whiletheoutputlayerrepresentstherecognitioncategories.Eachcategoryintheoutputlayerisassociatedwithaprototype,whichactsasareferenceforpatternmatching.Whenanewinputpatternispresented,ART-2calculatesthesimilaritybetweentheinputpatternandeachcategoryprototype,andassignsthepatterntothecategorywiththehighestmatch.Ifthesimilarityisbelowacertainthreshold,anewcategoryiscreated.
WhileART-2providesausefulframeworkforpatternrecognition,ithasseverallimitationsthathavemotivatedresearcherstoproposevariousimprovements.OnesuchenhancementistheFuzzyART(FART),whichintroducesafuzzylogiccomponenttoART-2.FARTallowsforpartialmatchesbetweeninputpatternsandcategoryprototypes,resultinginmoreflexibleandrobustrecognitioncapabilities.Moreover,FARTcanhandlenoisyorincompleteinputpatterns,makingitsuitableforreal-worldapplicationswheredatamaybeimperfect.
AnotherimprovementtoART-2istheART-2amodel,whichaddressessomeofthelimitationsoftheoriginalART-2byintroducingavigilanceparameter.Thevigilanceparametercontrolsthestrictnessofpatt