智能控制试卷及答案4套

附件1智能控制课程试题A附件1题号一二三四五六七总分分数合分人：复查人：一、填空题〔每空1分，共20分〕分数评卷人1．智能控制系统的根本类型有、、、、和。2．智能控制具有2个不同于常规控制的本质特点：和。3．一个理想的智能控制系统应具备的性能是、、、、等。4.人工神经网络常见的输出变换函数有：和。5.人工神经网络的学习规那么有：、和。6.在人工智能领域里知识表示可以分为和两类。二、简答题：〔每题5分，共30分〕分数评卷人1.智能控制系统应具有的特点是什么？2.智能控制系统的结构一般有哪几局部组成，它们之间存在什么关系？3.比拟智能控制与传统控制的特点。4．神经元计算与人工智能传统计算有什么不同？5．人工神经元网络的拓扑结构主要有哪几种？6．简述专家系统与传统程序的区别。三、作图题：〔每图4分，共20分〕分数评卷人1.画出以下应用场合下适当的隶属函数：〔a〕我们绝对相信附近的e(t)是“正小”，只有当e(t)足够远离时，我们才失去e(t)是“正小”的信心；〔b〕我们相信附近的e(t)是“正大”，而对于远离的e(t)我们很快失去信心；〔c〕随着e(t)从向左移动，我们很快失去信心，而随着e(t)从向右移动，我们较慢失去信心。2.画出以下两种情况的隶属函数：〔a〕精确集合的隶属函数；〔b〕写出单一模糊〔singletonfuzzification〕隶属函数的数学表达形式，并画出隶属函数图。四、计算题：〔每题10分，共20分〕分数评卷人1.一个模糊系统的输入和输出的隶属函数如图1所示。试计算以下条件和规那么的隶属函数：〔a〕规那么1：Iferroriszeroandchang-in-erroriszeroThenforceiszero。均使用最小化操作表示蕴含(usingminimumopertor)；〔b〕规那么2：Iferroriszeroandchang-in-errorispossmallThenforceisnegsmall。均使用乘积操作表示蕴含(usingproductopertor)；2.设论域，且试求〔补集〕，〔补集〕五、试论述对BP网络算法的改良。〔共10分〕分数评卷人附件1智能控制课程试题B附件1题号一二三四五六七总分分数合分人：复查人：一、填空题〔每空1分，共20分〕分数评卷人1．智能控制的研究对象具备的特点有：、和。2．智能控制系统的主要类型有：、、、、和。3．确定隶属函数的方法大致有、和。4.国内外学者提出了许多面向对象的神经网络控制结构和方法，从大类上看，较具代表性的有以下几种：、和。5.在一个神经网络中，常常根据处理单元的不同处理功能，将处理单元分成有以下三种：、和。6.专家系统具有三个重要的特征是：、和。二、简答题：〔每题5分，共30分〕分数评卷人智能控制有哪些应用领域？试举例说明其工作原理。试说明智能控制的三元结构，并画出展示它们之间关系的示意图。模糊逻辑与随机事件的联系与区别。给出典型的神经元模型。BP根本算法的优缺点。专家系统的根本组成。三、作图题：〔每图4分，共20分〕分数评卷人1.画出以下应用场合下适当的隶属函数：〔a〕随着e(t)从向左移动，我们很快失去信心，而随着e(t)从向右移动，我们较慢失去信心。〔b〕我们相信附近的e(t)是“正大”，而对于远离的e(t)我们很快失去信心；〔c〕我们绝对相信附近的e(t)是“正小”，只有当e(t)足够远离时，我们才失去e(t)是“正小”的信心；2.画出以下两种情况的隶属函数：〔a〕精确集合的隶属函数；〔b〕写出单一模糊〔singletonfuzzification〕隶属函数的数学表达形式，并画出隶属函数图。四、计算题：〔每题10分，共20分〕分数评卷人1.一个模糊系统的输入和输出的隶属函数如图1所示。试计算以下条件和规那么的隶属函数：〔a〕规那么1：Iferroriszeroandchang-in-errorisnegsmallThenforceispossmall。均使用最小化操作表示蕴含(usingminimumopertor)；〔b〕规那么2：Iferroriszeroandchang-in-errorispossmallThenforceisnegsmall。均使用乘积操作表示蕴含(usingproductopertor)；2.设论域，且试求〔补集〕，〔补集〕五、试论述建立专家系统的步骤。〔共10分〕分数评卷人附件1智能控制课程试题C附件1题号一二三四五六七总分分数合分人：复查人：一、填空题〔每空1分，共20分〕分数评卷人1．智能控制是一门新兴的学科，它具有非常广泛的应用领域，例如、、、和。2．传统控制包括和。3．一个理想的智能控制系统应具备的性能是、、、、等。4．学习系统的四个根本组成局部是、、、。5．专家系统的根本组成局部是、、。二、简答题：〔每题5分，共30分〕分数评卷人智能控制系统的结构一般有哪几局部组成，它们之间存在什么关系？智能控制系统有哪些类型，各自的特点是什么？比拟智能控制与传统控制的特点。4．根据外部环境所提供的知识信息与学习模块之间的相互作用方式，机器学习可以划分为哪几种方式？5．建造专家控制系统大体需要哪五个步骤？6．为了把专家系统技术应用于直接专家控制系统，在专家系统设计上必须遵循的原那么是什么？三、作图题：〔每图4分，共20分〕分数评卷人1.画出以下应用场合下适当的隶属函数：〔a〕我们绝对相信附近的e(t)是“正小”，只有当e(t)足够远离时，我们才失去e(t)是“正小”的信心；〔b〕我们相信附近的e(t)是“正大”，而对于远离的e(t)我们很快失去信心；〔c〕随着e(t)从向左移动，我们很快失去信心，而随着e(t)从向右移动，我们较慢失去信心。2.画出以下两种情况的隶属函数：〔a〕精确集合的隶属函数；〔b〕写出单一模糊〔singletonfuzzification〕隶属函数的数学表达形式，并画出隶属函数图。四、计算题：〔每题10分，共20分〕分数评卷人1.一个模糊系统的输入和输出的隶属函数如图1所示。试计算以下条件和规那么的隶属函数：〔a〕规那么1：Iferroriszeroandchang-in-erroriszeroThenforceiszero。均使用最小化操作表示蕴含(usingminimumopertor)；〔b〕规那么2：Iferroriszeroandchang-in-errorispossmallThenforceisnegsmall。均使用乘积操作表示蕴含(usingproductopertor)；2.设论域，且试求〔补集〕，〔补集〕五、画出静态多层前向人工神经网络〔BP网络〕的结构图，并简述BP神经网络的工作过程〔10分〕分数评卷人。附件1智能控制课程试题D附件1题号一二三四五六七总分分数合分人：复查人：一、填空题〔每空1分，共20分〕分数评卷人1．智能控制是一门新兴的学科，它具有非常广泛的应用领域，例如、、、和。2．智能控制系统的主要类型有：、、、、和。3．一个理想的智能控制系统应具备的性智能能是、、等。4．在设计知识表达方法时，必须从表达方法的、、这四个方面全面加以均衡考虑。5．在一个神经网络中，常常根据处理单元的不同处理功能，将处理单元分成输入单元、输出单元和三类。二、简答题：〔每题5分，共30分〕分数评卷人智能控制系统的结构一般有哪几局部组成，它们之间存在什么关系？试说明智能控制的三元结构，并画出展示它们之间关系的示意图。比拟智能控制与传统控制的特点。4．神经网络应具的四个根本属性是什么？5.神经网络的学习方法有哪些？6.按照专家系统所求解问题的性质，可分为哪几种类型？三、作图题：〔每图4分，共20分〕分数评卷人1.画出以下应用场合下适当的隶属函数：〔a〕我们绝对相信附近的e(t)是“正小”，只有当e(t)足够远离时，我们才失去e(t)是“正小”的信心；〔b〕我们相信附近的e(t)是“正大”，而对于远离的e(t)我们很快失去信心；〔c〕随着e(t)从向左移动，我们很快失去信心，而随着e(t)从向右移动，我们较慢失去信心。2.画出以下两种情况的隶属函数：〔a〕精确集合的隶属函数；〔b〕写出单一模糊〔singletonfuzzification〕隶属函数的数学表达形式，并画出隶属函数图。四、计算题：〔每题10分，共20分〕分数评卷人1.一个模糊系统的输入和输出的隶属函数如图1所示。试计算以下条件和规那么的隶属函数：〔a〕规那么1：Iferroriszeroandchang-in-erroriszeroThenforceiszero。均使用最小化操作表示蕴含(usingminimumopertor)；〔b〕规那么2：Iferroriszeroandchang-in-errorispossmallThenforceisnegsmall。均使用乘积操作表示蕴含(usingproductopertor)；2.设论域，且试求〔补集〕，〔补集〕五、试述专家控制系统的工作原理〔共10分〕分数评卷人Fuzzycontrolofaball-balancingsystemⅠ.IntroductionTheball-balancingsystemconsistsofacartwithanarcmadeoftwoparallelpipesonwhichasteelballrolls.Thecartmovesonapairoftrackshorizontallymountedonaheavysupport(Fig.1).Thecontrolobjectiveistobalancetheballonthetopofthearcandatthesametimeplacethecartinadesiredposition.Itiseducational,becausethelaboratoryrigissufficientlyslowforvisualinspectionofdifferentcontrolstrategiesandthemathematicalmodelissufficientlycomplextobechallenging.Itisaclassicalpendulumproblem,liketheonesusedasabenchmarkproblemforfuzzyandneuralnetcontrollers,assalesmaterialforfuzzydesigntools.Initially,thecartisinthemiddleofthetrackandtheballisontheleftsideofthecurvedarc.Acontrollerpullsthecartlefttogettheballupnearthemiddle,thenthecontrolleradjuststhecartpositionverycarefully,withoutloosingtheball.Fuzzycontrolprovidesaformatmethodologyforrepresenting,manipulatingandimplementingahuman’sheuristicknowledgeabouthowtocontrolasystem[1-3].Here,thefuzzycontroldesignmethodwillbeusedtocontroltheball-balancingsystem.Fig.1Ball-balancinglaboratoryrigⅡ.Designobjectivea).Learningtheoperatingprincipleoftheball-balancingsystem;b).Masteringthefuzzycontrolprincipleanddesignprocedure;c).Enhancingtheprogrammingpowerusingmatlab.Ⅲ.Designrequirementsa).Balancingtheballonthetopofthearcandatthesametimeplacethecartinadesiredposition.b).Comparingthecontrolresultofthelinearcontrollerwiththatofthefuzzycontrollerandthinkingabouttheadvantageoffuzzycontroltoconventionalcontrol.Ⅳ.Designprinciple①Modeldescriptionoftheball-balancingsystemIntroducethestatevectorofstatevariables(representscartpositionandrepresentsballangulardeviation)Thenonlinearstate-spaceequations[5]aregivenasfollows:Whererepresentscartradiusofthearc,isthecartweight,representscartdrivingforce,istheballradius,istheballrollingradius,istheballweight,istheballmomentofinertiaandrepresentsgravity.Themodelcanbelinearisedaroundtheorigin.Theapproximationstothetrigonometricfunctionsareintroducedasfollowsandthelinearstate-spacemodelcanbeobtainedasfollowsMatricesaresimplyandgivenasfollowswith,Theactualvaluesoftheconstantsare.②FuzzycontrollerdesignTherearespecificcomponentscharactersticofafuzzycontrollertosupportadesignprocedure.IntheblockdiagraminFig.2,thefuzzycontrollerhasfourmaincomponents.Thefollowingexplainstheblockdiagram.Fig.2FuzzycontrollerarchitectureFuzzificationThefirstcomponentisfuzzification,whichconvertseachpieceofinputdatatodegreesofmembershipbyalookupinoneofseveralmembershipfunctions.Thefuzzificationblockthusmatchestheinputdatawiththeconditionsoftherulestodeterminehowwelltheconditionofeachrulematchesthatparticularinputinstance.RulebaseTherulebasecontainsafuzzylogicquantificationoftheexpert’slinguisticdescriptionofhowtoachievegoodcontrol.c.InferenceengineForeachrule,theinferenceenginelooksupthemembershipvaluesintheconditionoftherule.AggregationTheaggregationoperationisusedwhencalculatingthedegreeoffulfillmentorfiringstrengthoftheconditionofarule.Aggregationisequivalenttofuzzification,whenthereisonlyoneinputtothecontroller.Aggreagtionissometimesalsocalledfufilmentoftheruleorfiringstrength.ActivationTheactivationofaruleisthedeductionoftheconclusion,possiblyreducedbyitsfiringstrength.Arulecanbeweightedbyaprioribyaweightingfactor,whichisitsdegreeofconfidence.Thedegreeofconfidenceisdeterminedbythedesigner,oralearningprogramtryingtoadapttherulestosomeinput-outputrelationship.AccumulationAllactivatedconclusionsareaccumulatedusingthemaxoperation.d.DefuzzificationTheresultingfuzzysetmustbeconvertedtoanumberthatcanbesenttotheprocessesasacontrolsignal.Thisoperationiscalleddefuzzification.Theoutputsetscanbesingletons,buttheycanalsobelinearcombinationsoftheinputs,orevenafunctionoftheinputs.TheT-SfuzzymodelwasproposedbyTakagiandSugenoinanefforttodevelopasystematicapproachtogeneratingfuzzyrulesfromagiveninput-outputdataset[4].Itsrulestructurehasthefollowingform:Whereisafuzzyset,istheinput,isthenumberofinputs，istheoutputspecifiedbytherule，isthetruthvalueparameter.Usingfuzzyinferencebaseduponproduct-sum-gravityatagiveninput,，thefinaloutputofthefuzzymodel,isinferredbytakingtheweightedaverageofwhereisthenumberoffuzzyrules,theweight,impliestheoveralltruthvalueoftherulecalculatedbasedonthedegreesofmembershipvalues:③ComputersimulationThesimulationresultscanbeobtainedbythedesignedprogramusingmatlab.Initialconditionscanbechangedandcontrollergainscanbeadjusted.Thenthedesiredresultscanbeobtained.Ⅴ.Designprocedurea).Themodeloftheball-balancingsystemhasbeengiven;b).Fuzzycontrollerdesign;Fuzzycontroldesignessentiallyamountsto(1)choosingthefuzzycontrollerinputsandoutputs(2)choosingthepreprocessingthatisneededforthecontrollerinputsandpossiblypostprocessingthatisneededfortheoutputs,and(3)designingeachofthefourcomponentsofthefuzzycontrollershowninFig.2.c).Computersimulation.References[1].K.M.PassinoandS.Yurkovich(1997).Fuzzycontrol,1stedn,AddisionWesleyLongman,Colifornia.[2].CaiZixing.IntelligentControl:Principles,TechniquesandApplications.Singapore-NewJersey:WorldScientificPublishers,Dec.1997.[3].Pedrycz,W.(1993).Fuzzycontrolandfuzzysystems,secondedn,WileyandSons,NewYork.[4].Takagi,T.andSugno,M.(1985).Fuzzyidentificationofsystemsanditsapplicationstomodelingandcontrol,IEEETrans.Systems,Man&Cybernetics15(1):116-132.SpeedcontroldesignforavehiclesystemusingfuzzylogicⅠ.IntroductionEngineandotherautomobilesystemsareincreasinglycontrolledelectronically.Thishasledtoimprovedfueleconomy,reducedpollution,

improveddrivingsafetyandreducedmanufacturingcosts.Howevertheautomobile

isahostileenvironment:especiallyintheenginecompartment,wherehightemperature,humidity,vibration,electricalinterferenceandafinecocktailofpotentiallycorrosivepollutantsarepresent.Thesehostilefactorsmaycauseelectricalcontactstodeteriorate,surfaceresistancestofallandsensitiveelectronicsystemstofailinavarietyofmodes.Someofthesefailuremodeswillbebenign,whereasothersmaybedangerousandcauseaccidentsandendangertohumanlife.Acruisecontrolsystem,orvehiclespeedcontrolsystemcankeepavehicle'sspeedconstantonlongrunsandthereforemayhelppreventdriverfatigue[2-5].Ifthedriverhandsoverspeedcontroltoacruisecontrolsystem,thenthecapabilityofthesystemtocontrolspeedtothesetvalueisjustascriticaltosafetyasisthecapabilityofthedrivertocontrolspeedmanually.Sothecruisecontrolsystemdesignisimperativeandimportanttoanautomobile.Ⅱ.Designrequirementsa).Designingcontrollerusingfuzzylogic;b).Makingtheautomobile’sspeedkeepconstant.Ⅲ.ModeldescriptionoftheautomobileThedynamicsoftheautomobile[1]aregivenasfollowsWhereisthecontrolinput(representsathrottleinputandrepresentsabrakeinput),isthemassofthevehicle,isitsaerodynamicdrag,isaconstantfrictionalforce,isthedriving/brakingforce,andsecissaturatedat).Wecanusefuzzycontrolmethodtodesignacruisecontrolsystem.Obviously,thefuzzycruisecontroldesignobjectiveistodevelopafuzzycontrollerthatregulatesavehicle’sspeedtoadriver-specifiedvalue.Ⅳ.SpeedcontroldesignusingfuzzylogicFuzzycontrollogicandneuralnetworksareotherexamplesofmethodologiescontrolengineersareexaminingtoaddressthecontrolofverycomplexsystems.Agoodfuzzycontrollogicapplicationisincruisecontrolarea.1)DesignofPIfuzzycontrollerSupposethatwewishtobeabletotrackasteporrampchangeinthedriver-specifiedspeedvalueveryaccurately.A“PIfuzzycontroller”canbeusedasshowninFig.1.InFig.1,thefuzzycontrollerisdenotedby;andarescalinggains;andistheinputoftheintegrator.Fig.1SpeedcontrolsystemusingaPIfuzzycontrollerFindthedifferentialequationthatdescribestheclosed-loopsystem.Letthestatebeandfindasystemofthreefirst-orderordinarydifferentialequationsthatcanbeusedbytheRunge-Kuttamethodinthesimulationoftheclosed-loopsystem.isusedtorepresentthecontrollerinthedifferentialequations.Forthereferenceinput,threedifferenttestsignalscanbeusedasfollows:a:Testinput1makes=18m/sec(40.3mph)forand＝22m/sec(49.2mph)for.b:Testinput2makes=18m/sec(40.3mph)forandincreaseslinearly(aramp)from18to22by,andthenfor.c:Testinput3makes=22forandweuseastheinitialcondition(thisrepresentsstartingthevehicleatrestandsuddenlycommandingalargeincreasespeed).Usefortestinput1and2.Designthefuzzycontrollertogetlessthan2%overshoot,arise-timebetween5and7sec,andasettlingtimeoflessthan8sec(i.e.,reachtowithin2%ofthefinalvaluewithin8sec)forthejumpfrom18to22in“testinput1”thatisdefinedabove.Also,fortherampinput(“testinput2”above)itmusthavelessthan1mph(0.447)steady-stateerror(i.e.,attheendoftheramppartoftheinputhavelessthan1mpherror).Fullyspecifythecontroller(e.g.,themembershipfunctions,rule-basedefuzzification,etc.)andsimulatetheclosed-loopsystemtodemonstratethatitperformsproperly.Provideplotsofandonthesameaxisandonadifferentplot.Fortestinput3findtherise-time,overshoot,2%settlingtime,andsteady-stateerrorfortheclosed-loopsystemforthecontrollerthatyoudesignedtomeetthespecificationsfortestinput1and2.UsingtheRunge-Kuttamethodandintegrationstepsizeof0.01,thesimulationresultscanbeshownasfollows.①．Testinput1Fig.2Vehiclespeedsandtheoutputoffuzzycontrollerusingtestinput1②．Testinput2Fig.3Vehiclespeedsandtheoutputoffuzzycontrollerusingtestinput2③．Testinput3Fig.4Vehiclespeedsandtheoutputoffuzzycontrollerusingtestinput32)DesignofPDfuzzycontrollerSupposethatyouareconcernedwithtrackingastepchangeinaccuratelyandthatyouusethePDfuzzycontrollershowninFig.5.Torepresentthederivative,simplyuseabackwarddifferenceWhereistheintegrationstepsizeinyoursimulation(oritcouldbeyoursamplingperiodinanimplementation).Fig.5SpeedcontrolsystemusingaPDfuzzycontrollerDesignaPDfuzzycontrollertogetlessthan2%overshoot,arise-timebetween7and10sec.andasettlingtimeoflessthan10secfortestinput1definedina).Also,fortherampinput(testinput2in1))itmusthavelessthan1mphsteady-stateerrortotheramp(i.e.,attheendoftheramppartoftheinput,havelessthan1mpherror).Fullyspecifyyourcontrollerandsimulatetheclosed-loopsystemtodemonstratethatitperformsproperly.Provideplotsofandonthesameaxisandonadifferentplot.Inthesimulations,theRunge-Kuttamethodisusedandanintegrationstepsizeof0.01.Assumethatfortestinputs1and2(henceweignorethederivativeinputincomingupwiththestateequationsfortheclosed-loopsystemandsimplyusetheapproximationforc(t)thatisshownabovesothatwehaveatwo-statesystem).Asafinaltestletandusetestinput3definedin1).①．Testinput1Fig.6Vehiclespeedsandtheoutputoffuzzycontrollerusingtestinput1②．Testinput2Fig.7Vehiclespeedsandtheoutputoffuzzycontrollerusingtestinput2③．Testinput3Fig.8Vehiclespeedsandtheoutputoffuzzycontrollerusingtestinput3Ⅴ.SummaryTokeepanautomobile’sspeedconstant,aspeedcontroldesignmethodusingfuzzylogicispresented.PIfuzzycontrollerandPDfuzzycontrollerdesignschemesaregiventoregulateavehicle’sspeedtoadriver-specifiedvalue.Thesimulationresultsshowthevalidityandoftheproposedtechnique.Thecontroldesignprocedurecanbesummarizedasfollows:ModelingandperformanceobjectivesBasically,theroleofmodelingafuzzycontroldesignisquite