电气工程及其自动化毕业设计论文外文翻译遗传算法在非线性模型中的应用模板.docx
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电气工程及其自动化毕业设计论文外文翻译遗传算法在非线性模型中的应用模板.docx
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电气工程及其自动化毕业设计论文外文翻译遗传算法在非线性模型中的应用模板
郑州航空工业管理学院
英文翻译
2011届电气工程及其自动化专业0706073班级
题目遗传算法在非线性模型中的应用
姓名学号
指导教师职称副教授
二О一一年三月三十日
英语原文:
ApplicationofGeneticProgrammingtoNonlinearModeling
Introduction
Identificationofnonlinearmodelswhicharebasedinpartatleastontheunderlyingphysicsoftherealsystempresentsmanyproblemssinceboththestructureandparametersofthemodelmayneedtobedetermined.Manymethodsexistfortheestimationofparametersfrommeasuresresponsedatabutstructuralidentificationismoredifficult.Oftenatrialanderrorapproachinvolvingacombinationofexpertknowledgeandexperimentalinvestigationisadoptedtochoosebetweenanumberofcandidatemodels.Possiblestructuresarededucedfromengineeringknowledgeofthesystemandtheparametersofthesemodelsareestimatedfromavailableexperimentaldata.Thisprocedureistimeconsumingandsub-optimal.Automationofthisprocesswouldmeanthatamuchlargerrangeofpotentialmodelstructurecouldbeinvestigatedmorequickly.
Geneticprogramming(GP)isanoptimizationmethodwhichcanbeusedtooptimizethenonlinearstructureofadynamicsystembyautomaticallyselectingmodelstructureelementsfromadatabaseandcombiningthemoptimallytoformacompletemathematicalmodel.Geneticprogrammingworksbyemulatingnaturalevolutiontogenerateamodelstructurethatmaximizes(orminimizes)someobjectivefunctioninvolvinganappropriatemeasureofthelevelofagreementbetweenthemodelandsystemresponse.Apopulationofmodelstructuresevolvesthroughmanygenerationstowardsasolutionusingcertainevolutionaryoperatorsanda“survival-of-the-fittest”selectionscheme.Theparametersofthesemodelsmaybeestimatedinaseparateandmoreconventionalphaseofthecompleteidentificationprocess.
Application
Geneticprogrammingisanestablishedtechniquewhichhasbeenappliedtoseveralnonlinearmodelingtasksincludingthedevelopmentofsignalprocessingalgorithmsandtheidentificationofchemicalprocesses.Intheidentificationofcontinuoustimesystemmodels,theapplicationofablockdiagramorientedsimulationapproachtoGPoptimizationisdiscussedbyMarenbach,BettenhausenandGray,andtheissuesinvolvedintheapplicationofGPtononlinearsystemidentificationarediscussedinGray’sanotherpaper.Inthispaper,Geneticprogrammingisappliedtotheidentificationofmodelstructuresfromexperimentaldata.Thesystemsunderinvestigationaretoberepresentedasnonlineartimedomaincontinuousdynamicmodels.
ThemodelstructureevolvesastheGPalgorithmminimizessomeobjectivefunctioninvolvinganappropriatemeasureofthelevelofagreementbetweenthemodelandsystemresponses.Oneexamplesis
(1)
WhereistheerrorbetweenmodeloutputandexperimentaldataforeachofNdatapoints.TheGPalgorithmconstructsandreconstructsmodelstructuresfromthefunctionlibrary.SimplexandsimulatedannealingmethodandthefitnessofthatmodelisevaluatedusingafitnessfunctionsuchasthatinEq.
(1).ThegeneralfitnessofthepopulationimprovesuntiltheGPeventuallyconvergestoamodeldescriptionofthesystem.
TheGeneticprogrammingalgorithm
Forthisresearch,asteady-stateGenetic-programmingalgorithmwasused.Ateachgeneration,twoparentsareselectedfromthepopulationandtheoffspringresultingfromtheircrossoveroperationreplaceanexistingmemberofthesamepopulation.Thenumberofcrossoveroperationsisequaltothesizeofthepopulationi.e.thecrossoverrateis100℅.Thecrossoveralgorithmusedwasasubtreecrossoverwithalimitonthedepthoftheresultingtree.
Geneticprogrammingparameterssuchasmutationrateandpopulationsizevariedaccordingtotheapplication.Moredifficultproblemswheretheexpectedmodelstructureiscomplexorwherethedataarenoisygenerallyrequirelargerpopulationsizes.Mutationratedidnotappeartohaveasignificanteffectforthesystemsinvestigatedduringthisresearch.Typically,avalueofabout2℅waschosen.
Thefunctionlibraryvariedaccordingtoapplicationrateandwhattypeofnonlinearitymightbeexpectedinthesystembeingidentified.Acoreoflinearblockswasalwaysavailable.Itwasfoundthatspecificnonlinearitysuchaslook-uptableswhichrepresentedaphysicalphenomenonwouldonlybeselectedbytheGeneticProgrammingalgorithmifthatnonlinearityactuallyexistedinthedynamicsystem.
Thisallowsthesystemtobetestedforspecificnonlinearities.
Programmingmodelstructureidentification
EachmemberoftheGeneticProgrammingpopulationrepresentsacandidatemodelforthesystem.Itisnecessarytoevaluateeachmodelandassigntoitsomefitnessvalue.Eachcandidateisintegratedusinganumericalintegrationroutinetoproduceatimeresponse.Thissimulationtimerespo
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- 关 键 词:
- 电气工程 及其 自动化 毕业设计 论文 外文 翻译 遗传 算法 非线性 模型 中的 应用 模板
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