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基于量化核最小均方算法的连续搅拌反应釜模型辨识(英文) Title:ModelIdentificationofContinuousStirred-TankReactorUsingQuantizedKernelLeastMeanSquareAlgorithm Abstract: Continuousstirred-tankreactors(CSTRs)arewidelyusedinvariouschemicalprocesses,includingpharmaceuticalproduction,biotechnology,andwastetreatment.AccuratelymodelingthedynamicbehaviorofCSTRsiscrucialforoptimizingtheirperformanceandcontrolstrategies.ThispaperpresentsanovelapproachformodelidentificationofCSTRsystemsusingthequantizedkernelleastmeansquare(QKLMS)algorithm.TheQKLMSalgorithmisadata-drivenmethodthatcombinesthebenefitsofquantizationandkernel-basedadaptivefiltering.Byadaptivelyadjustingthekernelsizeandquantizationbins,theQKLMSalgorithmcaneffectivelymodelthenonlinearandtime-varyingcharacteristicsofCSTRs. Introduction: CSTRsareimportantcomponentsinchemicalprocesses,andtheirdynamicbehaviorisinfluencedbyseveralfactorssuchasreactantfeedingrate,temperature,flowrate,andagitationspeed.AccuratemodelingofCSTRsisessentialforoptimizingprocessparameters,designingcontrolstrategies,andpredictingsystemresponses.Traditionalmodelingmethodsoftenrelyonsteady-stateassumptionsandlinearizationtechniques,whichmaynotcapturethetruedynamicsofCSTRsaccurately.Therefore,data-drivenapproacheshavegainedattentioninrecentyearsduetotheirabilitytocapturenonlinearandtime-varyingbehaviorsofCSTRsystems. Methods: TheproposedapproachutilizestheQKLMSalgorithmformodelidentificationofCSTRs.TheQKLMSalgorithmaddressesthelimitationsoftraditionalkernelmethodsbyintroducingquantization,whichreducesthecomputationalcomplexitywhilepreservingimportantsystemdynamics.TheQKLMSalgorithmiterativelyupdatesthefiltercoefficientsbasedonthequantizationerrorandweightedkernelfunctions.ThekernelfunctionscapturethenonlinearityofCSTRs,whilethequantizationerrorensuresconvergenceandstabilityofthemodelidentificationprocess. ResultsandDiscussion: ToevaluatetheeffectivenessoftheQKLMSalgorithmforCSTRmodeling,experimentaldatafromarealCSTRsystemwereused.TheperformanceoftheQKLMSalgorithmwascomparedwitho