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一种新的基于权重系数的证据理论合成方法 Abstract: Inthispaper,weintroduceanewmethodforsynthesizingtheevidencetheorybasedonweightcoefficients.Weprovideadetailedexplanationoftheprinciplesunderlyingourmethodanddemonstrateitseffectivenessinreal-worldapplications.Theproposedmethodaddressesthelimitationsofexistingmethods,namely,theirinabilitytohandlecomplexanddiversedatasets.Inourproposedmethod,weintroduceweightcoefficientstoreflecttheimportanceofthevariouspiecesofevidenceintheoverallfusionprocess.Wepresentexperimentalresultsthatdemonstratethevalidityandefficiencyofourproposedmethod. Introduction: Theevidencetheoryisapowerfultoolforfusingdatafrommultiplesources.Itsabilitytocopewithuncertainandconflictingdatamakesitparticularlyusefulinavarietyofdomains,includingremotesensing,decision-making,andartificialintelligence.Inrecentyears,therehavebeensignificanteffortstoimprovethistheoryanddevelopnewmethodsforcombiningevidence.However,existingapproacheshavelimitations,particularlyinhandlingcomplexdatasets. Inthispaper,weproposeanewmethodforsynthesizingevidencetheorybasedonweightcoefficients.Weintroduceaconceptofweightcoefficienttoreflecttheimportanceofindividualpiecesofevidenceintheoverallfusionprocess.Theproposedmethodovercomesthelimitationsofexistingmethodsbyallowingtheweightsofvariouspiecesofevidencetobeassignedbasedontheirimportanceandreliability. Methodology: Ourproposedmethodinvolvesfoursteps:datapre-processing,evidencecombination,weightassignment,anddecision-making.Inthedatapre-processingstep,wenormalizeandstandardizetheinputdata.Intheevidencecombinationstep,weapplytheevidencetheorytocombinetheindividualpiecesofevidence.Intheweightassignmentstep,weusetheweightcoefficienttoreflecttheimportanceofindividualpiecesofevidence.Inthedecision-makingstep,weusetheweightedevidencetomakeafinaldecision. Theweightcoefficientusesasimpleformula,whichisbasedonthecovariancematrix.Theformulainvolvescomputingthecovariancematrixoftheinputdataandusingittocomputetheweightingcoefficients.Theformulaensuresthattheweight

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