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基于区间数距离的IGOWLA算子的区间型组合预测模型.docx

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基于区间数距离的IGOWLA算子的区间型组合预测模型 Abstract Theinterval-typecombinationpredictionmodelbasedonintervalnumberdistanceIGOWLAoperatorisproposedinthispaper.Thismodelisaimedatsolvingtheuncertaintyandvaguenessproblemsinthecombinationpredictionofintervalvariables.Basedontheintervalnumberdistance,theIGOWLAoperatorisintroducedtocalculatetheweightofeachinputintervalvariable.Then,theweightedaveragingoperatorisusedtocombinetheintervalvariablesandobtainthefinaloutputintervalprediction.Theeffectivenessandsuperiorityoftheproposedmodelaredemonstratedthroughnumericalexamplesandcomparisonwithotherpredictionmodels. Keywords:interval-typecombinationprediction;intervalnumberdistance;IGOWLAoperator;weightedaveragingoperator Introduction Theintervalvariablehasbeenwidelyusedinvariousfieldsduetoitsabilitytoexpressuncertaintyandvagueness.Interval-valueddataprovidemoreinformationthanasinglepointvalue,andcaneffectivelydealwiththeincomplete,imprecise,andindeterminateinformationinreal-worldproblems.However,thecombinationpredictionofintervalvariablesisstillachallengingissueduetothecomplexityoftheintervaldata.Traditionalpredictionmodels,suchasregressionanalysisandtimeseriesanalysis,arenotsuitableforintervaldataanalysisandpredictionduetotheirunderlyingassumptions. Inrecentyears,manyresearchershaveproposedvariousintervalpredictionmodelsbasedonfuzzylogic,neuralnetworks,andgreysystems.Thesemodelscandealwithintervaldatatosomeextent,buttheystillfacesomechallengesinpredictingtheintervalvariablesaccuratelyandrobustly.Oneofthemainchallengesistheselectionofappropriateoperatorstocombinetheinputvariablesintoafinalprediction.Mostoftheexistingcombinationoperatorsarebasedonmathematicalmodels,andtheycannotaccuratelyreflecttheweightoftheinputvariablesinthecombinationprocess. Toovercomethischallenge,thispaperproposesaninterval-typecombinationpredictionmodelbasedonintervalnumberdistanceIGOWLAoperator.TheIGOWLAoperatorisintroducedtocalculatetheweightofeachinputintervalvariablebasedonthedistancebetweentheintervalnumbers.Then,t