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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

EXPLORATIONANDSIMULATIONOFNEURALNETWORKPIDINTEMPERATURECONTROLSYSTEMABSTRACTTHISPAPERPRESENTSANEWKINDOFINTELLIGENCEPIDCONTROLMETHODONBPNEURALNETWORKANDSOMEOFBASICCONCEPTSABOUTBPNEURALNETWORKNEURALNETWORKINTELLIGENCEPIDCONTROLLERHASMANYADVANCEDPROPERTIESCOMPAREDWITHTRADITIONALPIDCONTROLLERTHEBPNEURALNETWORKPIDCONTROLMETHODISAPPLIEDTOTEMPERATURECONTROLSYSTEMININDUSTRYFIELDTHESIMULATIONRESULTSSHOWTHATTHECONTROLMETHODHASHIGHCONTROLACCURACY,STRONGADAPTATIONANDEXCELLENTCONTROLRESULTSKEYWORDSNEURALNETWORK,PIDCONTROLLER,TEMPERATURECONTROLSYSTEM1FOREWORDININDUSTRIALPROCESSCONTROL,PIDCONTROLISABASICCONTROLMETHOD,ITSROBUSTNESS,SIMPLESTRUCTURE,EASYTOIMPLEMENT,BUTTHECONVENTIONALPIDCONTROLALSOHASITSOWNDISADVANTAGE,BECAUSETHEPARAMETERSOFCONVENTIONALPIDCONTROLLERISBASEDONBEINGMATHEMATICALMODELOFCONTROLLEDOBJECTIDENTIFIED,WHENTHEMATHEMATICALMODELOFTHEOBJECTARECHANGING,NONLINEARTIME,PIDPARAMETERSISNOTEASYINACCORDANCEWITHITSACTUALSITUATIONANDMAKEADJUSTMENTS,THEIMPACTOFTHEQUALITYCONTROLSOTHATTHECONTROLOFTHEQUALITYCONTROLSYSTEMDECLINEESPECIALLYINTHEPURETIMEDELAYCHARACTERISTICSWITHTHEINDUSTRIALPROCESS,THECONVENTIONALPIDCONTROLMOREDIFFICULTTOMEETTHEREQUIREMENTSOFTHECONTROLACCURACYBECAUSEOFNEURALNETWORKSWITHSELFORGANIZATION,SELFLEARNING,ADAPTIVECAPACITY,INTHISPAPER,BASEDONBPNEURALNETWORKPIDCONTROLLER,SOTHATARTIFICIALNEURALNETWORKPIDCONTROLWITHTHETRADITIONALCOMBINATIONOFEACHOTHERANDJOINTLYIMPROVEQUALITYCONTROLANDTOTHEMETHODINTHETEMPERATURECONTROLSYSTEMUSINGTHESIMULATIONLANGUAGEMATLABAPPLICATION2BPNEURALNETWORKMODELANDALGORITHMCONSTITUTE21BPNEURALNETWORKMODELCONSTITUTEBPNEURALNETWORKLEARNINGPROCESSCONSTITUTEDMAINLYBYTWOSTAGESTHEFIRSTPHASEFORWARDPROPAGATION,THEINPUTSIGNALTHROUGHTHEINPUTLAYER,HIDDENLAYERAFTERLAYERBYLAYERTREATMENT,INTHEOUTPUTLAYERISCALCULATEDFOREACHNEURONTHEACTUALOUTPUTVALUETHESECONDSTAGETHEPROCESSOFERRORBACKPROPAGATION,IFNOTINTHEOUTPUTLAYERTHEDESIREDOUTPUTVALUE,THEACTUALLAYERBYLAYERRECURSIVEOUTPUTANDDESIREDOUTPUTOFTHEMARGIN,ANDTHERIGHTTOADJUSTTHEBASISOFTHISERRORFACTOR22THENEURALNETWORKPIDCONTROLLERSTRUCTUREANDALGORITHMINTHETRADITIONALPIDCONTROL,CLASSICALINCREMENTALPIDCONTROLFORMSUKUK1PEKEK1IEKDEK2EK1EK2KPPROPORTIONALCOEFFICIENTIIOPINTEGRALCOEFFICIENTODPDDIFFERENTIALCOEFFICIENTSETUPBPNEURALNETWORKPIDCONTROLLERSTRUCTURERKEKUKYK_YKADAPTIVEINORDERTOACHIEVEDIP,,OFTHEPURPOSE,THEOUTPUTLAYERFORTHETHREENEURONS,CORRESPONDINGTODIP,,INPUTLAYER,HIDDENLAYERNEURONS,THENUMBEROFCHARGEDOBJECTSINACCORDANCEWITHTHECOMPLEXITYOFFIXEDHIDDENLAYERACTIVATIONFUNCTIONUSEDFORTHEPOSITIVEANDNEGATIVESYMMETRICALSIGMOIDFUNCTIONXXXXEEEEXXFTANHOUTPUTLAYERACTIVATIONFUNCTIONOFTHEUSEOFNONNEGATIVESIGMOIDFUNCTIONXXXEEEXXG2TANH1WEASSUMETHATO31,O32,O33ISTHEOUTPUTOFOUTPUTLAYER,WHICHCORRESPONDTOP,I,DWETAKETHEPERFORMANCEINDEXFUNCTIONASFOLLOWS21121KYKRJWHENTHEACTUALOUTPUTANDTHEDEVIATIONBETWEENTHEDESIREDOUTPUT,THENTHEERRORBACKPROPAGATIONREVERSETHESPREADOFTHESUBSTANCEISBYADJUSTINGTHEWEIGHTSSOTHATTHESMALLESTDEVIATION,ITCANUSETHESTEEPESTDESCENTMETHOD,ERRORFUNCTIONBYANEGATIVEGRADIENTDIRECTIONTOALLLEVELSOFNEURONWEIGHTSTOADJUSTORAMENDTHENHAVENNPIDNNPLANTNNARITHMETIC13KWLI33KWWJLILILEARNINGRATEMOMENTUMOFAVAILABLEBYTHECHAINRULE3LIWJ3333311LILLLLWKNETKNETOOKUKUKYKYJEK1333331LILLLLWKNETKNETOOKUKUKYONEL1,2,3SOBPNEURALNETWORKCANBETHEOUTPUTLAYERWEIGHTSOFTHECALCULATIONFORMULA13233KWKOKWLIILLIOFWHICH1SGN13,33KNETGKOKUKUKYKELLLBECAUSEOFTHEPIDCONTROL1KUKYALGORITHMINNORMALCIRCUMSTANCESAREUNKNOWN,CANBEUSEDTOREPLACEFUNCTIONSYMBOLS1SGNKUKY,ANDTHROUGHADJUSTMENTSTOCORRECTERRORSEMPATHYCANBEHIDDENLAYERWEIGHTCOEFFICIENTCALCULATIONFORMULA2122KWKOWIJJIIJOFWHICH331322KWKNETFLILLII,INTHEABOVEVARIOUSTYPES,THESCORNER1,2,3EXPRESS,RESPECTIVELY,INPUTLAYER,HIDDENLAYER,OUTPUTLAYER,LTHENUMBEROFOUTPUTLAYERNEURONSITHENUMBEROFHIDDENLAYERNEURONSJTHENUMBEROFINPUTLAYERNEURONS1XGXGG‘2/12XFF‘BASEDONTHEABOVECANBEBPNEURALNETWORKCONTROLALGORITHMS1DETERMINETHENEURALNETWORKARCHITECTURE,INITIALIZEDWEIGHTSONEACHFLOORCONTROLTHEVOLUMEOFOUTPUT,ERRORCHECKTHEINITIALVALUE02OFTHESAMPLINGSYSTEMHASBEENKR、KYCALCULATEDBYTHEERRORKYKRKEANDUNDERTHEINCREMENTALPIDALGORITHMTOTHEERRORCOMPONENTINPUTLAYERASINPUT3ACCORDINGTOALLFLOORSOFTHEWEIGHTCOEFFICIENTSARECALCULATEDLAYERSBPNEURALNETWORKINPUTANDOUTPUTOUTPUTLAYERWEIGHT,RESPECTIVELYKP、KI、KDACCORDINGTOINCREMENTALPIDCONTROLLERFORMULACANBEOUTPUTU4WILLSERVEUASTHESUPERVISIONOFBPNEURALNETWORKSIGNAL,TOTHEBACKPROPAGATIONALGORITHMBPONLINEACCORDINGTOTHEOUTPUTLAYER,HIDDENLAYEROFTHELEARNINGALGORITHMADJUSTTHEWEIGHTSONEACHFLOOR,SOTHATTOACHIEVEADAPTIVEADJUSTPIDCOEFFICIENTS5BACKTO23INTHETEMPERATURECONTROLSYSTEMSIMULATIONEXPERIMENTINTHEINDUSTRIALPRODUCTIONPROCESS,CONTROLTHEPRODUCTIONPROCESSOFALLKINDS,OFTENTOTHETEMPERATUREOFTHEPROCESSSUCHASTIMEDELAYCONTROLOFTHEPROCESSSETTHETEMPERATURECONTROLWASCHARGEDWITHTHEPROCESSOFTRANSFERFUNCTIONIS1101403SSSGSE60THESIMULATIONRESULTSASFOLLOWSFIGURE1FIGURE2FIGURE1FORTHECONVENTIONALPIDCONTROL,FIG2FORTHEBPNEURALNETWORKPIDCONTROLFROMTHEFIGUREWECANSEETHATCONVENTIONALPIDCONTROLARISINGFROMOVERSHOOTANDTRANSITIONTIMETHANTHEBPNEURALNETWORKPIDCONTROLARISINGFROMOVERSHOOTANDTHETRANSITIONTIMEISMUCHGREATER,WHICHCANBESEENBPNEURALNETWORKPIDCONTROLSTRONGSELFADAPTABILITYANDHIGHCONTROLPRECISION4CONCLUDINGREMARKSINTHISPAPER,THEROOTCONTROLOFBPNEURALNETWORKALGORITHMTOTIMEDELAYTHETEMPERATURECONTROLSYSTEMSIMULATION,EXPERIMENTALRESULTSSHOWTHATTHEBPNEURAL

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