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1、Subject:ImprovementofBPAlgorithmandResearchonItsApplicationinthePIDOptimizationControlSpecialty:AppliedMathematicsName:LiHu(Signature)Instructor:LiJunmin(Signature)ABSTRACTProportional-Integral-Derivativecontrol,abbreviatedasPIDcontrol,isakindofmechanism
2、offeedbackcontrolthatistoreducetheerrorbyproportionalfunction,toeliminatestaticdeviationbytheintegralcalculusfunctionandtopredictfuturebythedifferentialcalculusfunction.Becauseofitsclearprinciple,simplestructureandeasyimplementation,ithasbeenwidelyusedin
3、thecontrolfield.ThekeytotheapplicationofthePIDcontrolleristoselectappropriateparametersinordertoadapttodifferentobjects.Butforanobjectwithtime-varyingandlargetimedelaycharacteristics,thePIDcontroller’seffectisnotveryideal,evenhardtoachievecontrolrequirem
4、ents.ThemainreasonisthatcontrolparametersofthePIDcontrollerdonothavethecapabilityofjustifyingtheparametersonline.Asanintelligentalgorithm,theBPneuralnetworkalgorithmisanapplicationmoreextensivelyusedinthefieldofartificialintelligence.Itisakindoferrorback
5、propagationalgorithmaccordingtothetrainingofthemultilayernetwork,hastheexcellentcapacityoflearningandadaption.Inthispaper,akindofimprovedBPalgorithmisputforward,andthisBPalgorithmisusedinthechoiceofPIDparameters,torealizetheparameterssettingandonlineadap
6、tion.BecausetheinitialweightsoftheBPnetworkisselectedatrandom,thiswillgreatlyaffectthestabilityofthenetwork,thus,inthispaper,theoptimizationfunctionoftheadaptivegeneticalgorithmisusedby,puttingforwardanalgorithmbasedonadaptivegeneticalgorithmtooptimizeth
7、eweightsoftheBPneuralnetwork,inordertoimprovetheparameterself-tuningandtheadaptiveabilityofthePIDcontroller.ThispaperistostudythebasicprincipleandthestructureoftheBPneuralnetwork,toanalyzethelimitationsoftheBPalgorithmandtomakeproperimprovementstothebasi
8、cBPalgorithm.Accordingtotheoptimizationcharacteristicofthegeneticalgorithm,thispaperistotrainthebestweightsoftheBPnetwork,andtocombinetheimprovedalgorithmwiththePIDcontrol,torealizetheon-linetuningofthePIDcontroller,sothat
