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英文翻译--神经网络PID在温度控制系统中的研究与仿真.doc

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英文翻译--神经网络PID在温度控制系统中的研究与仿真.doc

ExplorationAndSimulationofNeuralNetworkPIDInTemperatureControlSystemAbstractThispaperpresentsanewkindofintelligencePIDcontrolmethodonBPneuralnetworkandsomeofbasicconceptsaboutBPneuralnetwork.NeuralnetworkintelligencePIDcontrollerhasmanyadvancedpropertiescomparedwithtraditionalPIDcontroller.TheBPneuralnetworkPIDcontrolmethodisappliedtotemperaturecontrolsysteminindustryfield.Thesimulationresultsshowthatthecontrolmethodhashighcontrolaccuracy,strongadaptationandexcellentcontrolresults.KeywordsNeuralnetwork,PIDcontroller,Temperaturecontrolsystem1ForewordInindustrialprocesscontrol,PIDcontrolisabasiccontrolmethod,itsrobustness,simplestructure,easytoimplement,buttheconventionalPIDcontrolalsohasitsowndisadvantage,becausetheparametersofconventionalPIDcontrollerisbasedonbeingmathematicalmodelofcontrolledobjectidentified,whenthemathematicalmodeloftheobjectarechanging,nonlineartime,PIDparametersisnoteasyinaccordancewithitsactualsituationandmakeadjustments,theimpactofthequalitycontrolsothatthecontrolofthequalitycontrolsystemdecline.Especiallyinthepuretimedelaycharacteristicswiththeindustrialprocess,theconventionalPIDcontrolmoredifficulttomeettherequirementsofthecontrolaccuracy.Becauseofneuralnetworkswithselforganization,selflearning,adaptivecapacity,Inthispaper,basedonBPneuralnetworkPIDcontroller,sothatartificialneuralnetworkPIDcontrolwiththetraditionalcombinationofeachotherandjointlyimprovequalitycontrolandtothemethodinthetemperaturecontrolsystemusingthesimulationlanguageMatlabapplication.2BPneuralnetworkmodelandalgorithmconstitute2.1BPneuralnetworkmodelconstituteBPneuralnetworklearningprocessconstitutedmainlybytwostagesThefirstphaseforwardpropagation,theinputsignalthroughtheinputlayer,hiddenlayerafterlayerbylayertreatment,intheoutputlayeriscalculatedforeachneurontheactualoutputvalue.Thesecondstagetheprocessoferrorbackpropagation,ifnotintheoutputlayerthedesiredoutputvalue,theactuallayerbylayerrecursiveoutputanddesiredoutputofthemargin,andtherighttoadjustthebasisofthiserrorfactor.2.2TheneuralnetworkPIDcontrollerstructureandalgorithmInthetraditionalPIDcontrol,classicalincrementalPIDcontrolformsukuk1pekek1iekdek2ek1ek2KpproportionalcoefficientiiopIntegralcoefficientodpdDifferentialcoefficientSetupBPneuralnetworkPIDcontrollerstructurerkekukyk_ykAdaptiveinordertoachievedip,,ofthepurpose,theoutputlayerforthethreeneurons,correspondingtodip,,.Inputlayer,hiddenlayerneurons,thenumberofchargedobjectsinaccordancewiththecomplexityoffixed.HiddenlayeractivationfunctionusedforthepositiveandnegativesymmetricalsigmoidfunctionxxxxeeeexxftanhOutputlayeractivationfunctionoftheuseofnonnegativesigmoidfunctionxxxeeexxg2tanh1Weassumethato31,o32,o33istheoutputofoutputlayer,whichcorrespondtop,i,d.Wetaketheperformanceindexfunctionasfollows21121kykrJWhentheactualoutputandthedeviationbetweenthedesiredoutput,thentheerrorbackpropagation.Reversethespreadofthesubstanceisbyadjustingtheweightssothatthesmallestdeviation,itcanusethesteepestdescentmethod,errorfunctionbyanegativegradientdirectiontoalllevelsofneuronweightstoadjustoramend.ThenhaveNNPIDNNPlantNNArithmetic13kwli33kwwJliliLearningrateMomentumofAvailablebythechainrule3liwJ3333311lillllwknetknetOOkukukykyJek1333331lillllwknetknetOOkukukyOnel1,2,3SoBPneuralnetworkcanbetheoutputlayerweightsofthecalculationformula13233kwkOkwliilliOfwhich1sgn13,33knetgkOkukukykelllBecauseofthePIDcontrol1kukyalgorithminnormalcircumstancesareunknown,canbeusedtoreplacefunctionsymbols1sgnkuky,andthroughadjustmentstocorrecterrors.Empathycanbehiddenlayerweightcoefficientcalculationformula2122kwkOwijjiijOfwhich331322kwknetflillii,Intheabovevarioustypes,theScorner1,2,3express,respectively,inputlayer,hiddenlayer,outputlayer,lThenumberofoutputlayerneuronsiThenumberofhiddenlayerneuronsjThenumberofinputlayerneurons1xgxgg2/12xffBasedontheabovecanbeBPneuralnetworkcontrolalgorithms1determinetheneuralnetworkarchitecture,initializedweightsoneachfloor.Controlthevolumeofoutput,errorchecktheinitialvalue0.2ofthesamplingsystemhasbeenkr、ky.Calculatedbytheerrorkykrke.AndundertheincrementalPIDalgorithmtotheerrorcomponentinputlayerasinput.3AccordingtoallfloorsoftheweightcoefficientsarecalculatedlayersBPneuralnetworkinputandoutput.Outputlayerweight,respectivelyKp、Ki、Kd.AccordingtoincrementalPIDcontrollerformulacanbeoutputu.4willserveuasthesupervisionofBPneuralnetworksignal,tothebackpropagationalgorithmBP.Onlineaccordingtotheoutputlayer,hiddenlayerofthelearningalgorithmadjusttheweightsoneachfloor,sothattoachieveadaptiveadjustPIDcoefficients.5backto2.3.InthetemperaturecontrolsystemsimulationexperimentIntheindustrialproductionprocess,controltheproductionprocessofallkinds,oftentothetemperatureoftheprocesssuchastimedelaycontroloftheprocess.Setthetemperaturecontrolwaschargedwiththeprocessoftransferfunctionis1101403sssGSe60ThesimulationresultsasfollowsFigure1Figure2Figure1fortheconventionalPIDcontrol,Fig2FortheBPneuralnetworkPIDcontrol.FromthefigurewecanseethatconventionalPIDcontrolarisingfromovershootandtransitiontimethantheBPneuralnetworkPIDcontrolarisingfromovershootandthetransitiontimeismuchgreater,whichcanbeseenBPneuralnetworkPIDcontrolstrongselfadaptabilityandhighcontrolprecision.4.ConcludingremarksInthispaper,therootcontrolofBPneuralnetworkalgorithmtotimedelaythetemperaturecontrolsystemsimulation,experimentalresultsshowthattheBPneural

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