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1、ATransportModeSelectionMethodforMultimodalTransportationbasedonanAdaptiveANNSystemFourthInternationalConferenceonNaturalComputationATransportModeSelectionMethodforMultimodalTransportationbasedonanAdaptiveANNSystemLiliQu,YanChen,XiangweiMuDalianMaritimeUniversi
2、ty,P.R.China,116026qulili_dlmu@1634>>TheavailabilityofpowerfulprocessingcapacityAbstractandadvantageincapturingthedecisionmakers’(DM)behaviormakeneuralnetworksasthepromisingtoolsMultimodaltransportationisacomplexnetwork,inofmachinelearningtobeappliedinMCDM[5][
3、6].whichallthecomponentsshouldbeseamlesslylinkedMalakootiproposedresultsonusingneuralnetworksandefficientlycoordinated.ConsideredmanytosolveMCDM[7].Especially,feedforwardartificialnoncommensurable,nonlinearevenconflictingcriterianeuralnetworks(FANN)areusuallyu
4、sedtoevaluatesimultaneously,thetransportmodeselectioninalternatives.multimodaltransportationisstudiedwithintheTheFANNmethodforsolvingMCDMconsistsofframeworkofmulticriteriadecisionmaking(MCDM).twoparts:1)IncorporateMCDMbasicdefinitionsandThetheoreticalbasisforf
5、eedforwardartificialneuralpropertiesintoFANN.Inordertochoosethesatisfiednetwork(FANN)tosolvethisMCDMproblemisalternativebyusingtheFANNmodeltosolveMCDM,presented.Withtheinitialtopologypredeterminedbywesetuptheoreticalbasisandpropositions.2)fuzzyanalysishierarch
6、yprocess(AHP),anadaptiveOptimizeANNsystemtoestablishanadaptiveFANNANNsystemisproposed,inwhichthenumberofANN[8][9],optimizetheneuralnetworkstructure,inputnodesadaptsthedecisionmakers’preferencenormalizeinputnodesvalue,increasethedatasamplesthresholdandtheinitia
7、linputweightsaredeterminedintrainingsetdynamicallyanddeterminethebyfuzzyAHP.Empiricalresultsevidentlyshowthisappropriateinput-hiddeninitialweights.MCDMmethodisanaccurate,flexibleandefficientInordertosearchforthe“best”transportmodeofatransportmodeselectionmodel
8、.multimodaltransportationnetworkinanefficientandeffectiveway,thispaperdemonstratestheimportant1.IntroductionroleofANNinMCDMproblem.Therestofthispaperisorganizedasfollows:TosolveMCD
