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基于递归模糊神经网络的污水处理控制方法.docx

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基于递归模糊神经网络的污水处理控制方法 Abstract Inrecentyears,waterpollutionhasbecomeanincreasinglypressingissueworldwide.Onemajorcontributortowaterpollutionisuntreatedsewage.Therefore,effectivesewagetreatmentiscrucialformitigatingwaterpollution.Traditionalsewagetreatmenttechnologiesusefixedcontrolmethods,whichmaynotbeefficientoreffectiveundervaryingconditions.Inthispaper,weproposeanewapproachtosewagetreatmentcontrolusingarecursivefuzzyneuralnetwork.Theproposedapproachiscapableofoptimalcontrolunderdifferentoperatingscenariosbyeffectivelyhandlinguncertaintiesinsewagetreatmentsystems.Weevaluatetheefficacyoftheproposedapproachusingsimulationexperimentsandshowthatitoutperformsexistingcontrolmethods. Introduction Sewagetreatmentistheprocessofremovingcontaminantsfromwastewaterbeforeitisdischargedintotheenvironment.Theaimofsewagetreatmentistoprotecthumanhealthandthenaturalenvironment.Mostsewagetreatmentplantsusemultiple-stagemethodstotreatwastewater,includingphysical,chemical,andbiologicalprocesses. Traditionally,controlofsewagetreatmentplantsisachievedthroughfixedcontrolmethods,whichareoptimizedforaspecificoperatingscenario.However,thesecontrolmethodsarerelativelyrigidandmaynotbeeffectiveorefficientundervaryingoperatingconditions.Asaresult,anewapproachtosewagetreatmentplantcontrolisrequired,onethatcaneffectivelyhandletheuncertaintyandvariationinherentinsewagetreatmentsystems. Fuzzyneuralnetworks(FNNs)areaclassofartificialneuralnetworksthatusefuzzylogictohandleuncertainties.FNNsarecapableoflearningandmodelingcomplexnonlinearsystems,makingthemanattractivesolutionforsewagetreatmentplantcontrol.RecursiveFNNs(RFNNs)areasubclassofFNNsthatareparticularlysuitablefordynamicsystems.RFNNshavetheabilitytorecursivelymodifytheirstructureandparameters,makingthemhighlyadaptivetochangingoperatingconditions. Inthispaper,weproposeanewapproachforsewagetreatmentplantcontrolusingarecursiveFNN.Theproposedapproachiscapableofoptimalcontrolunderdifferentoperatingscenariosbyeffectivelyhandlinguncertaintiesinsewagetre