基于IDPC-RVM的多模态间歇过程质量变量在线预测-豆柴文库

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基于IDPC-RVM的多模态间歇过程质量变量在线预测
Title:OnlinePredictionofIntermittentProcessQualityVariablesusingIDPC-RVMinaMultimodalContext
Abstract:
Inthemanufacturingindustry,theabilitytoaccuratelypredictthequalityvariablesofintermittentprocessesplaysacrucialroleinensuringefficientproductionandreducingwasteanddefects.ThispaperproposesanonlinepredictionframeworkbasedonIncrementalandDecrementalProbabilisticClassificationwithRelevanceVectorMachines(IDPC-RVM)totacklethechallengesofpredictingqualityvariablesinamultimodalcontext.TheIDPC-RVMalgorithmleveragesthebenefitsofRVMandtheabilitytohandleincrementalanddecrementallearningforonlineprediction.Theproposedframeworkaimstoimproveaccuracy,speed,andadaptability,ultimatelyenhancingtheefficiencyofintermittentprocessqualitycontrol.
Introduction:
Qualitycontrolisparamountinthemanufacturingindustrytomeetcustomerdemandsandmaintainacompetitiveedge.Intermittentprocesses,characterizedbyvaryingandunpredictableperformance,poseuniquechallengesforqualitycontrol.Conventionalofflinepredictionmodelsoftenfailtocapturethedynamicnatureoftheseprocesses.ThispaperaddressesthisissuebyintroducinganonlinepredictionframeworkbasedontheIDPC-RVMalgorithm.
Methodology:
Theproposedframeworkconsistsofthreestages:datacollectionandpreprocessing,modeltraining,andonlineprediction.
1.DataCollectionandPreprocessing:
Thequalityvariablesdata,includingbothcontinuousandcategoricaldata,arecollectedfromvarioussensorsandmonitoringdevicesinreal-time.Thepreprocessingstageinvolvesdatacleaning,featureextraction,andnormalizationtoensuredataconsistencyandsuitabilityforthemodel.
2.ModelTraining:
TheIDPC-RVMalgorithmisutilizedforpredictioninthemultimodalcontext.RVMisasparseBayesianlearningmethodthatprovidesaccuratepredictionswithfewersupportvectors.Ithandlesbothincrementalanddecrementallearning,makingitidealforonlineprediction.Thetrainingprocessinvolvesinitializingthemodelandupdatingitincrementallyordecrementallyasnewdatabecomesavailable.
3.OnlinePrediction:
Oncethemodelistrained,itcanbeapplied