简介:Thispaperpresentsanewfuzzymultiplecriteria(bothqualitativeandquantitative)decision-making(MCDM)methodbasedonfuzzyrelationaldegreeanalysis.Theconceptsoffuzzysettheoryareusedtoconstructaweightedsuitabilitydecisionmatrixtoevaluatetheweightedsuitabilityofdifferentalternativesversusvariouscriteria.Thepositiveidealsolutionandnegativeidealsolutionarethenobtainedbyusingamethodofrankingfuzzynumbers,andthefuzzyrelationaldegreesofdifferentalternativesversuspositiveidealsolutionandnegativeidealsolutionarecalculatedbyusingtheproposedarithmetic.Finally,therelativerelationaldegreesofvariousalternativesversuspositiveidealsolutionarerankedtodeterminethebestalternative.Anumericalexampleisprovidedtoillustratetheproposedmethodattheendofthispaper.
简介:Syllogisticfuzzyreasoningisintroducedintofuzzysystem,andthenewCascadedFuzzySystem(CFS)ispresented.ThethoroughlytheoreticalanalysisandexperimentalresultsshowthatsyllogisticfuzzyreasoningismorerobustthanallotherimplicationinferencesfornoisedataandthatCFShasbetterrobustnessthanconventionalfuzzysystems,whichprovidethesolidfoundationforCFS'spotentialapplicationinfuzzycontrolandmodelingandsoon.
简介:Thispaperproposesanewneuralfuzzyinferencesystemthatmainlyconsistsoffourparts.Thefirstpartisabouthowtouseneuralnetworktoexpresstherelationwithinafuzzyrule.Thesecondpartisthesimplificationofthefirstpart,andexperimentsshowthatthesesimplificationswork.Onthecontrarytothesecondpart,thethirdpartistheenhancementofthefirstpartanditcanbeusedwhenthefirstpartcannotworkverywellinthefuzzyinferencealgorithm,whichwouldbeintroducedinthefourthpart.Finally,thefourthpart"neuralfuzzyinferencealgorithm"isbeenintroduced.Itcaninferencethenewmembershipfunctionoftheoutputbasedonpreviousfuzzyrules.Theaccuracyofthefuzzyinferencealgorithmisdependentonneuralnetworkgeneralizationability.Evenifthegeneralizationabilityoftheneuralnetworkweusedisgood,westillgetinaccurateresultssincethenewcomingrulemaynotberelatedtoanyofthepreviousrules.Experimentsshowthisalgorithmissuccessfulinsituationswhichsatisfytheseconditions.
简介:Ahybridapproachforfuzzysystemdesignbasedonclusteringandakindofneurofuzzynetworksisproposed.Anunsupervisedclusteringtechniqueisfirstlyusedtodeterminethenumberofif-thenfuzzyrulesandgenerateaninitialfuzzyrulebasefromthegiveninput-outputdata.Then,aclassofneurofuzzynetworksisconstructedanditsweightsaretunedsothattheobtainedfuzzyrulebasehasahighaccuracy.Finally,twoexamplesoffunctionapproximationproblemsaregiventoillustratetheeffectivenessoftheproposedapproach.
简介:Dempster-Shaferevidencetheory(DStheory)iswidelyusedinbrainmagneticresonanceimaging(MRI)segmentation,duetoitsefficientcombinationoftheevidencefromdifferentsources.Inthispaper,animprovedMRIsegmentationmethod,whichisbasedonfuzzyc-means(FCM)andDStheory,isproposed.Firstly,theaveragefusionmethodisusedtoreducetheuncertaintyandtheconflictinformationinthepictures.Then,theneighborhoodinformationandthedifferentinfluencesofspatiallocationofneighborhoodpixelsaretakenintoconsiderationtohandlethespatialinformation.Finally,thesegmentationandthesensordatafusionareachievedbyusingtheDStheory.ThesimulatedimagesandtheMRIimagesillustratethatourproposedmethodismoreeffectiveinimagesegmentation.
简介:特征选择(FS)指选择给定的outcome.Unlike是很预兆的那些输入属性的过程另外的维数减小方法,在FS的reduction.The好处是双重的以后,特征选购者保存特征的原来的意思:它更加减少正式就职算法的跑的时间,并且增加在用模糊逻辑和模糊逻辑的乳房X线照片分类的FS过程基于的产生model.This纸分析的精确性Quickreduct算法被申请从特征的FS提取了使用在乳房X线照片region.The上构造的灰色的水平co-occurence矩阵(GLCM)预兆的精确性特征用NaiveBayes,Ripper,C4.5,和蚂蚁矿工algo被测试
简介:Amongtheavailableclusteringalgorithmsindatamining,theCLOPEalgorithmattractsmuchmoreattentionwithitshighspeedandgoodperformance.However,theproperchoiceofsomeparametersintheCLOPEalgorithmdirectlyaffectsthevalidityoftheclusteringresults,whichisstillanopenissue.Forthispurpose,thispaperproposesafuzzyCLOPEalgorithm,andpresentsamethodfortheoptimalparameterchoicebydefiningamodifiedpartitionfuzzydegreeasaclusteringvalidityfunction.TheexperimentalresultswithrealdatasetillustratetheeffectivenessoftheproposedfuzzyCLOPEalgorithmandoptimalparameterchoicemethodbasedonthemodifiedpartitionfuzzydegree.
简介:ThesecondaryusageofspectrumhasbeeninvestigatedinCognitiveRadio(CR)networktoresolvingthespectrumscarcityissueinwirelesscommunication.WhenPrimaryUsers(PU)whoownthespectrumappear,spectrumhandoffisneededtomaintainthecommunicationsofSecondaryUsers.ButthedecisionmakingofspectrumhandoffisachallengeissueforCRnetwork,becausetheinputofdecisionmaking,whichobtainthroughspectrumsensing,isheterogeneousandinexact.Inthispaperwewillusefuzzylogiccontroltheorytosolvethisissueandmakeuseofnewinformationforhandoffoperation:theprobabilityofPU'soccupancyatacertainchannel.Ournewalgorithmcanmakemoreintelligentdecisioncomparedtosimpletraditionalspectrumhandoffdecisionmakingandreducetheprobabilityofspectrumhandoff,alsotheperformanceofSU'scommunicationcanbeenhanced.
简介:Torealizetheon-linemeasurementandmakeanalysisonthedensityofalgaeandtheirclusterdistribution,thefluorescentdetectionandfuzzypatternrecognitiontechniquesareused.Theprincipleoffluorescentfiber-opticdetectionisgivenaswellasthemethodoffuzzyfeatureextractionusingaclassofneuralnetwork.
简介:Thispaperproposedafuzzifyfunctorasanextensionoftheconceptoffuzzysets.Thefuzzifyfunctorandthefirst-orderoperatedfuzzysetaredefined.Fromthetheoryanalysis,itcanbeobservedthatwhenthefuzzifyfunctoractsonasimplecrispset,wegetthefirstorderfuzzysetortype-1fuzzyset.Byoperatingthefuzzifyfunctoronfuzzysets,wegetthehigherorderfuzzysetsorhighertypefuzzysetsandtheirmembershipfunctions.Usingthefuzzifyfunctorwecanexactlydescribethetype-1fuzz...
简介:Thispaperpresentsaweb-basedsystemtopredicttheelectricityprices.Theproposedsystemcapturesthegeographicallocation,weatherforecast,andoilpriceforoneweekahead.Thecapturedparametersarefedtoafuzzy-logic-basedalgorithmtocalculateelectricenergyprices.Basedonpredictedelectricityprices,consumerscanturnON/OFForrescheduleoperationsoftheirhomeappliancestoreducetheirelectricitybill.Theproposedalgorithmwasdevelopedandhostedinautilityserver(U-server).Ontheconsumerside,ahomegateway(H-gateway),andamonitoringandcontrolsystemwasdesigned,built,andtestedbyusingasinglechipmicrocontroller.
简介:Makingfulluseofwindpowerisoneofthemainpurposesofthewindturbinegeneratorcontrol.Conventionalhillclimbingsearch(HCS)methodcanrealizethemaximumpowerpointtracking(MPPT).However,thestepsizeofHCSmethodisconstantsothatitcannotconsiderbothsteady-stateresponseanddynamicresponse.Afuzzylogicalcontrol(FLC)algorithmisproposedtosolvethisprobleminthispaper,whichcantrackthemaximumpowerpoint(MPP)quicklyandsmoothly.ToevaluateMPPTalgorithms,fourperformanceindicesarealsoproposedinthispaper.Theyaretheenergycapturedbywindturbine,themaximumpower-pointtrackingtimewhenwindspeedchangesslowly,thefluctuationmagnitudeofrealpowerduringsteadystate,andtheenergycapturedbywindturbinewhenwindspeedchangesfast.ThreecasesaredesignedandsimulatedinMATLAB/Simulinkrespectively.ThecomparisonofthethreeMPPTstrategiesconcludesthattheproposedfuzzylogicalcontrolalgorithmismoresuperiortotheconventionalHCSalgorithms.