6. fuzzy inferences ifk15037, 3 credits · rinaldi munir stei itb 7 fuzzyficapon fuzzy logic...
TRANSCRIPT
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HISTORY
YuitaArumSari,S.Kom,M.Kom 2
ProfessorLoKiA.Zadeh
In1965,Lo,iA.ZadehoftheUniversityofCaliforniaatBerkeleypublished"FuzzySets,"whichlaidoutthemathemaJcsoffuzzysettheoryand,byextension,fuzzylogic.ZadehhadobservedthatconvenJonalcomputerlogiccouldnotmanipulatedatathatrepresentedsubjecJveorvagueideas,sohecreatedfuzzylogictoallowcomputerstodeterminethedisJncJonsamongdatawithshadesofgray,similartotheprocessofhumanreasoning.hNp://www.cs.berkeley.edu/~zadeh/
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ExampleofApplicaPons
MasseyUniversity3
• InthecityofSendaiinJapan,a16-staJonsubwaysystemiscontrolledbyafuzzycomputer(SeijiYasunobuandSojiMiyamotoofHitachi)–therideissosmooth,ridersdonotneedtoholdstraps
• Nissan–fuzzyautomaJctransmission,fuzzyanJ-skidbrakingsystem• CSK,Hitachi–Hand-wriJngRecogniJon• Sony-Hand-printedcharacterrecogniJon• Ricoh,Hitachi–VoicerecogniJon• Tokyo’sstockmarkethashadatleastonestock-tradingporKoliobasedon
FuzzyLogicthatoutperformedtheNikkeiexchangeaverage• NASAhasstudiedfuzzycontrolforautomatedspacedocking:simulaJons
showthatafuzzycontrolsystemcangreatlyreducefuelconsumpJon• Canondevelopedanauto-focusingcamerathatusesacharge-coupled
device(CCD)tomeasuretheclarityoftheimageinsixregionsofitsfieldofviewandusetheinformaJonprovidedtodetermineiftheimageisinfocus.Italsotrackstherateofchangeoflensmovementduringfocusing,andcontrolsitsspeedtopreventovershoot.
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IntroducPonofFIS
IndianInsPtuteofTechnologyDelhi4
● AFuzzyInferenceSystem(FIS)isawayofmappinganinputspacetoanoutputspaceusingfuzzylogic
● FIS uses a collecJon of fuzzy membership funcJonsandrules,insteadofBooleanlogic.
● The rules in FIS (someJmes may be called as fuzzyexpertsystem)arefuzzyproducJonrulesoftheform:− ifpthenq,wherepandqarefuzzystatements.
● Forexample,inafuzzyrule− ifxislowandyishighthenzismedium.− Here x is low; y is high; z is medium are fuzzystatements;xandyareinputvariables;zisanoutputvariable,low,high,andmediumarefuzzysets.
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IntroducPonofFIS(cont.)
IndianInsPtuteofTechnologyDelhi5
● The antecedent describes towhat degree the ruleapplies, while the conclusion assigns a fuzzyfuncJontoeachofoneormoreoutputvariables.● Most tools for working with fuzzy expert systemsallowmorethanoneconclusionperrule.● Thesetofrulesinafuzzyexpertsystemisknownasknowledgebase.
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StructureofFuzzyExpertSystems
RinaldiMunirSTEIITB6
KnowledgeBase
InferenceEngine
FuzzificaPon
DefuzzficaPon
Crispvalue
Crispvalue
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FuncPonalProcess
RinaldiMunirSTEIITB7
FuzzyficaPon
FuzzyLogicOperaPon
ImplicaPon
AgregaPon
DefuzzyficaPon
INPUT
OUTPUT
CrispvalueàfuzzymembershipfuncJon
AntecedentmaybejoinedbyOR;ANDoperators
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FuzzyficaPon
RinaldiMunirSTEIITB8
Fuzzyfikasi:prosesmemetakannilaicrisp(numerik)kedalamhimpunanfuzzydanmenentukanderajatkeanggotaannyadidalamhimpunanfuzzy.Halinidilakukankarenadatadiprosesberdasarkanteorihimpunanfuzzysehinggadatayangbukandalambentukfuzzyharusdiubahkedalambentukfuzzy.
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Example:FuzzyficaPon
RinaldiMunirSTEIITB9
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Example:FuzzyficaPon
Sumber:SriKusumaDewi/AplikasiLogikaFu7zzy10
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FuzzyMembershipFuncPon
FuzzyLogicwithEngineeringApplicaJons:TimothyJ.Ross11
TypesofMembershipFuncJons•ThemostcommonlyusedinpracJceare–Triangles–Trapezoids–Bellcurves–Gaussian,and–Sigmoidal
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FuzzyMembershipFuncPon
FuzzyLogicwithEngineeringApplicaJons:TimothyJ.Ross12
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FuzzyMembershipFuncPon
FuzzyLogic:Intelligence,Control,andInformaJon,J.YenandR.Langari,PrenJceHall13
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FuzzyMembershipFuncPon
FuzzyLogic:Intelligence,Control,andInformaJon,J.YenandR.Langari,PrenJceHall14
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FuzzyMembershipFuncPon
FuzzyLogic:Intelligence,Control,andInformaJon,J.YenandR.Langari,PrenJceHall15
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FuzzyMembershipFuncPon
FuzzyLogic:Intelligence,Control,andInformaJon,J.YenandR.Langari,PrenJceHall16
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FuzzyMembershipFuncPon
FuzzyLogic:Intelligence,Control,andInformaJon,J.YenandR.Langari,PrenJceHall17
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FuzzyMembershipFuncPon
FuzzyLogic:Intelligence,Control,andInformaJon,J.YenandR.Langari,PrenJceHall18
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FuzzyLogicOperaPon
Sumber:SriKusumaDewi/AplikasiLogikaFu7zzy19
Jikabagianantesendendihubungkanolehkonektorand,or,dannot,makaderajatkebenarannyadihitungdenganoperasifuzzyyangbersesuaian
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ImplicaPon
Sumber:SriKusumaDewi/AplikasiLogikaFu7zzy20
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ImplicaPon
Wikipedia21
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ImplicaPon
Sumber:SriKusumaDewi/AplikasiLogikaFu7zzy22
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AggregaPon
RinaldiMunir23
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AggregaPon
RinaldiMunir24
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AggregaPon
RinaldiMunir25
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AggregaPon
RinaldiMunir26
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AggregaPon
RinaldiMunir27
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AggregaPon
RinaldiMunir28
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AggregaPon
Mathworks29
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DefuzzyficaPon
Mathworks30
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DefuzzyficaPon
RinaldiMunir31
• Strategiyangumumdipakaidalamdefuzzifikasiadalahmenentukanbentukkompromiterbaik.
• Metode-metodeuntukstrategiiniadalah:1. Metodekeanggotaanmaximum(max-membership)2. Metodepusatluas(CenterofArea,CoA).3. Metodekeanggotaanmaksimumrata-rata(Mean-max
MembershipatauMiddle-of-Maxima)
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DefuzzyficaPon
RinaldiMunir32
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DefuzzyficaPon
RinaldiMunir33
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DefuzzyficaPon
RinaldiMunir34
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RinaldiMunir35
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RinaldiMunir36
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RinaldiMunir37
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RinaldiMunir38
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RinaldiMunir39
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RinaldiMunir41
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RinaldiMunir42
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RinaldiMunir43
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RinaldiMunir44
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RinaldiMunir45
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RinaldiMunir46
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RinaldiMunir47
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YuitaArumSari,S.Kom,M.Kom 48
Thank