在神经网络中起重机传输遗传算法最佳化外文文献翻译、中英文翻译

附录AGeneticAlgorithmOptimizationOnCraneTransmissionInNeuralNetworkAbstract：Thefuzzyoptimizationmathematicmodelisestablishedtodesigncranetransmission.Themethodofsecond-classcomprehensiveevaluationwasusedbytheoptimallevelcutset,thustheoptimallevelvalueofeveryfuzzyconstraintcanbeattained,andthefuzzyoptimizationistransformedintotheusualoptimization.TheFastBackPropagationofneuralnetworkalgorithmisadoptedtotrainfeed-forwardnetworkssoastofitrelativecoefficient.Thenthefitnessfunctionwithpenaltytermsisbuiltbypenaltystrategy,neuralnetworksprogramisrecalled,solverfunctionsofGeneticAlgorithmToolboxofMatlabsoftwareareadoptedtosolvetheoptimizationmathematicmodel.Index：Terms-CraneMechanism;GeneticAlgorithmOptimization;NeuralNetworks.FUZZYOPTIMIZATIONMATHEMATICALMODELOFTRACTIONMECHANISMDesignsample:Ainvolutehelicoidalwormgearingisadoptedincranetransmission,whichhasprincipalparametersasfollows:ratedpowerPe=1.5kw,outputspeed28.4r/min,outputtorqueT2=295.87N.m,gearratiou=49.3,workingloadfactork=1.05.Thewormismachinedandheat-treated45steelandthetoothcoronaofgearismadeofmachinedZQA19-4A.SpecifyingobjectivefunctionInordertoeconomizenonferrousmetaloftoothcoronaofwormgear,theobjectivefunctionshouldbespecifiedthatthevolumeoftoothcoronaofwormgearintractionmechanisminclinetominimum=1.5.AccordingtoFig.1,d0,di2andbareouterdiameters,innerdiametersandfacewidthoftoothcoronaofwormgearrespectively,thusthevolumeoftoothcoronais;whereThereforetheobjectivefunctioniswherem-moduleofgear;d1-referencediameterofgear;z1-numberofstartofworm.Fig.1.theconfiguredrawingofwormgearB.SelectingdesignvariablesAccordingtoequationoftheobjectivefunction,Z1,m,d1shouldbeselectedasdesignvariables,thatis:C.EstablishingfuzzyconstraintsConsideringtherandomcharacterofthevalueofdesignparametersandsomefactorswhosevalueisveryindefinitesuchasloadingpropertyandmaterialquality,thefuzzyconstraintsaresetup,includingthepropertyandboundaryconstraints.1)Limitofnumberofstartofworm:topowerdrive,z1=1~2;2)Limitofmoduleofgear:2≤m≤8;3)Limitoftheleadangleγofthewormforguaranteeingtheefficiencyofthewormgearing:3≤γ≤8,tanγ=mz1/d1;4)Constraintofcontactstrengthofwormgear:WhereZe-thematerialelasticityfactor,σh-thecontactstressesofwormgear;[σh]-thefuzzyvalueoftheallowablecontactstressesofwormgear.5)Constraintoftoothbeamstrengthofwormgear:Whereσfthebeamstressofgearteeth;[σf]-fuzzyvalueoftheallowablebendingstressofwormgearteeth;Yf-theprofilefactorforwormgearteeth.6)Constraintofthestiffnessofworm:Thewormissupportedbetweentwobearings,ifthewormshaftbendstoomuch,thatis,theteethwillnotmeshproperly,andtheresultwillbeexcessivewearandearlyfailure.SothemaximumdeflectionisWhereFt1-thetangentialforceoftheworm(N),Fr1-theradialforceoftheworm(N),E-themodulusofelasticity(Mpa),I-theinertiamomentofthedangerouscross-sectionofworm(mm4)L-thedistanceofthewormbearings(mm),L=0.9muz1.IV.FUZZYOPTIMIZATIONMATHEMATICALMODELOFTRACTIONMECHANISMThekeyofthismethodishowtodecidetheoptimallevelvalue.Severalfactors,suchasfactorclass,factorfuzzinessandthedifferentinfluenceofthefactorsonthedifferentoptimallevelvalues,wereconsideredandthemethodofsecond-classcomprehensiveevaluationwasusedbasedontheoptimallevelcutset,thustheoptimallevelvalueλ*ofeveryfuzzyconstraintcanbeattained,thatisλ*=0.71.Thereforethefuzzyoptimizationproblemisconvertedintotheusualoptimizationproblem.V.TRAININGRELATIONCOEFFICIENTBYNEURALNETWORKSNeuralnetworksarecomposedofsimpleelementoperatinginparallel.Theseelementsareinspiredbybiologicalnervoussystems.Asinnature,thenetworkfunctionisdeterminedlargelybytheconnectionsbetweenelements.Wecantrainaneuralnetworktoperformaparticularfunctionbyadjustingthevaluesoftheconnections(weights)betweenelements.Commonlyneuralnetworksareadjusted,ortrained,sothataparticularinputleadstoaspecifictargetoutputbasedonacomparisonoftheoutputandthetarget,untilthenetworkoutputmatchesthetarget.SomepointsonrelationcurvebetweenteethnumberZ2andtheprofilefactorYfofwormgearareselectedastrainingsampledata,theFastBackPropagationareadoptedtotrainfeed-forwardnetworks,theweightsandbiasesofthenetworkareupdated.ThenneuralnetworksissimulatedbythefunctionofNeuralNetworksToolboxinMATLAB.Programasfollows:Z2=0:10:90;YF=[2.58,2.5176,2.4566,2.3972,2.3392,2.2825,2.2273,2.1734,2.1208,2.0695]n1=5;[W1,b1,W2,b2]=initff(Z2,n1,’tansig’,YF,’purelin’);fpd=100;mne=20000;sse=0.001;lr=0.01;tp=[fpd,mne,sse,lr];[W1,b1,W2,b2,te,tr]=trainbpx(W1,b1,’tansig’,W2,b2,’purelin’,Z2,YF,tp)y=simuff(Z2,W1,b1,’tansig’,W2,b2,’purelin’)VI..SOLVINGUSUALOPTIMIZATIONMATHEMATICALMODELBYGENETICALGORITHMTOOLBOXOnekeytosuccessfullysolvingmanytypesofoptimizationproblemsischoosingthemethodthatbestsuitstheproblem.TheGeneticAlgorithmandDirectSearchToolboxisacollectionoffunctionsthatextendthecapabilitiesoftheOptimizationToolboxandtheMATLAB?numericcomputingenvironment.TheGeneticAlgorithmToolboxincludesroutinesforsolvingoptimizationproblemsusingGeneticalgorithmDirectsearch.ThesealgorithmsenableyoutosolveavarietyofoptimizationproblemsthatlieoutsidethescopeofthestandardOptimizationToolbox.Firstlythefitnessfunctionwithpenaltytermsisbuiltbypenaltystrategywithadditiontype,andthefitnessfunctionisprogrammedinMATLABlanguage,andaboveneuralnetworksprogramfittingtheprofilefactorofwormgearteethisrecalled,thenthenonlinearconstraintsfunctionareprogrammedandthesolverfunctionsofGeneticAlgorithmToolboxareadopted.Programasfollows:options=gaoptimset(‘PopulationSize’,20);options=gaoptimset(‘Generations',100);options=gaoptimset(‘CrossoverFraction’0.95,’MigrationFraction’0.01);options=gaoptimset('SelectionFcn',selection-tournament,’CrossoverFcn’,crossoverscattered,’MutationFcn’,Mutationgaussian);nvars=3;lb=[1;2;10];ub=[2;8;150];[x,Fval,exitFlag,Output]=ga(@fitnessfun,nvars,[],[],[],[],lb,ub,@yueshufun,options)Afterfunctioncounting108timesanditerating326times,thefinalrunningoutputofaboveprogrammingis:x1=1.0102,x2=4.8889,x3=78.2222,f(X)=1090628.VII..CONCLUSIONThispaperexploredthemethodsavailableintheGeneticAlgorithmandNeuralNetworksToolbox.Comparedwithstandardoptimizationalgorithms(f(X)=1269257.5),theobjectivefunctionoptimuminthegeneticalgorithmisabout16.37%lessthantheformer.Thereforewesawthatthegeneticalgorithmisaneffectivesolverfornonsmoothproblems.Additionally,wefoundthatthegeneticalgorithmcanbecombinedwithothersolvers,suchasfuzzylogicandneuralnetworks,toefficientlyfindamoreaccuratesolution.TABLEIOUTPUTOFSTANDARDOPTIMIZATIONANDGENETICALGORITHM

附录B在神经网络中起重机传输遗传算法最佳化摘要：那失真的适宜数学模型在设计起重机传输建立。那方法的二等的综合评价被那最佳的把割集弄平整使用经由，那方法的二等的综合评价是使用经由那最佳的把割集弄平整，因此每个模糊约束那最佳的价值可以是获得弄平整，并且那模糊的最佳化是被变成那通常的最佳化。神经网络算法那背面加固增长的将采用到连续性前馈网络如此适合相关系数。然后那用罚款期限是构成由罚款策略装配功能、神经网络计划是召回、解算机功能的遗传算法工具箱的matlab软件是采用到解决那最佳化数学模型。索引词：起重机机构；遗传算法最佳化；神经网络。模糊的最佳化数学模型的牵引机构设计渐开线螺旋状的蜗轮传动装置是采民用在起重机传输，哪个有主参数如下：额定功率Pe=1.5kw、输出速度28.4r／min、输出转矩T2=2295.87n.m、齿轮比U=49.3、工作负荷因素k=1.05,那螺旋是机器和经加热处理材45钢和那由ZQA19-4构成的齿轮的齿轮冠.A指定目标函数为了节省有色金属的齿轮冠的螺旋齿轮，那目标函数将应指定那那大量的齿轮冠的螺旋齿轮在牵引机构向最小的按照图1倾斜，d0、di2,b分别是外径、内径和螺旋齿轮的齿面宽冠，因此那是大量的牙齿冠；由所以那目标函数是m齿轮模数；d1齿轮分度圆直径；z1螺旋开始的齿数。图1.涡轮传动装置图B反面选择设计参数按照等式的那目标函数、,m、d1将应虽然设计参数选择，但是简而言之：C建立模糊约束认为Φ值的随机特性设计参数和一些因素谁的价值很不定的比如负荷性质和材料品质、那模糊约束是建立、包括那性质和边界约束在内。1）极限的开始的螺旋的齿数：z1=1~2;；2）极限的齿轮的模数:2≤m≤8；3）极限的那导程角螺旋的因为保证蜗轮传动装置的效率：3≤γ≤8,tanγ=mz1/d1;4）约束的接触强度的螺旋齿轮：那材料弹性因素、σh那接触应力的螺旋齿轮；[σh]-那模糊的Φ值那容许接触应力的螺旋齿轮。5）约束的牙齿梁强度的螺旋齿轮：那横梁强调的轮齿；[σf]-模糊的Φ值那容许弯曲应力的螺旋齿轮牙齿；Yf-那轮廓因素因为螺旋齿轮牙齿。６）约束稠的的的螺旋：那螺旋信息系统支持在...之间二轴承、如果那蜗杆轴弯曲多，那就是说，那牙齿不会适当地网孔，那么，那结果将要成为...的过度磨损和过早损坏所以极限偏转是ft1-螺旋的切向力（N）、Fr1-螺旋的径向力（N）、E-那弹性模数（Mpa)、I-危险截面的惯性矩的螺旋（mm4）L-蜗杆轴承的距离（毫米）、L=9muz1IV.模糊的最佳化数学模型的牵引机构这个的键方法是如何决定那最佳的把价值个别的因素弄平整、比如因素通信链路分析器系统、因素模糊和因素的不同的影响上去那不同的最佳的把价值弄平整、是认为和那方法的二等的综合评价是使用以那最佳的把割集弄平整为基础，因此那最佳的把价值＊的每模糊约束可以是获得弄平整、简而言之＊那模糊的优化问题是变为那通常的优化问题。V.连续性相关系数由神经网络神经网络由...组成简单的元件并行操作这个元件被生物学的神经的体系当做本质上鼓舞、那网络函数决意大量地由那关系在...之间元件我们可以训练一神经网络执行一特定函数由调整那Φ值那关系（重量）在...之间元件通常神经网络是调整，否则连续性，结果一特别的输入导致一具体任务产量以一比较产量的和那靶子为基础、直到那网络产量相配那靶子。一些涨若干点相关曲线在齿数和那轮廓因素的螺旋齿轮被选为连续性样本数据之间、那背面加固繁殖将采用到连续性前馈网络、网络的重量和偏见是更新然后神经网络被那功能的神经网络工具箱在matlab模拟。计划如下：Z2=0:10:90;YF=[2.58,2.5176,2.4566,2.3972,2.3392,2.2825,2.2273,2.1734,2.1208,2.0695];n1=5;[W1,b1,W2,b2]=initff(Z2,n1,’tansig’,YF,’purelin’);fpd=100;mne=20000;sse=0.001;lr=0.01;tp=[fpd,