DOC PREVIEW
CALVIN ENGR 315 - Fuzzy Logic and Fuzzy Control Systems

This preview shows page 1-2-3-4 out of 11 pages.

Save
View full document
View full document
Premium Document
Do you want full access? Go Premium and unlock all 11 pages.
Access to all documents
Download any document
Ad free experience
View full document
Premium Document
Do you want full access? Go Premium and unlock all 11 pages.
Access to all documents
Download any document
Ad free experience
View full document
Premium Document
Do you want full access? Go Premium and unlock all 11 pages.
Access to all documents
Download any document
Ad free experience
View full document
Premium Document
Do you want full access? Go Premium and unlock all 11 pages.
Access to all documents
Download any document
Ad free experience
Premium Document
Do you want full access? Go Premium and unlock all 11 pages.
Access to all documents
Download any document
Ad free experience

Unformatted text preview:

Ryan JohnsonI. IntroductionII. HistoryIII. Fuzzy LogicIV. Building a Fuzzy SystemFigure 3.2: Output SubsetsFigure 3.3: Fuzzy Rules DefinedV. System Functionality & the Fuzzy ProcessFigure 4.3: Panel OffsetFigure 4.4: Gaussian Input Membership Function ShapesFigure 4.5: New Membership Rules AddedFigure 4.6: Fine AdjustmentVI. ConclusionReferencesFUZZY LOGIC AND FUZZY LOGIC SUNTRACKING CONTROLRYAN JOHNSONDECEMBER 17, 2002CALVIN COLLEGEENGR315AABSTRACT:Fuzzy logic isa rule-baseddecision processthat seeks to solveproblems where thesystem is difficult tomodel and whereambiguity orvagueness isabundant betweentwo extremes.Fuzzy logic allowsthe system to bedefined by logicequations ratherthan complexdifferentialequations andcomes from athinking thatidentifies and takesadvantage of thegrayness betweenthe two extremes.Fuzzy logic systemsare composed offuzzy subsets andfuzzy rules. Thefuzzy subsetsrepresent differentsubsets of the inputand outputvariables. The fuzzyrules relate theinput variables tothe output variablesvia the subsets.Given a set of fuzzyrules, the systemcan compensatequickly andefficiently. Thoughthe Western worlddid not initiallyaccept fuzzy logicand fuzzy ideas,today fuzzy logic isapplied in manysystems. In thisresearch paper, asolar power suntracking system isimplemented usingfuzzy logic. Thesteps of how tocreate a fuzzysystem aredescribed as well asthe description ofhow the fuzzysystem works. Keywords:membershipfunction, grayness,fuzzy subsets,fuzzification, fuzzyrules,defuzzification,FuzzyApproximationTheorem (FAT),fuzzy numbers, andfuzzy systemsI.INTRODUCTIONHow do wedefine the world welive in today? Howdo we see thingsaround us? Most ofus are taught from avery young age tolook at the world interms of black andwhite, A-or-not-A,Boolean 1’s and 0’s.Much of science,math, logic, andeven cultureassume a world of1’s and 0’s, true orfalse, hot or cold, A-or-not-A. Tochallenge this typeof thinking, considera half eaten apple.Is it half there orhalf gone? Is theglass half full or halfempty? Is the cargoing fast or slow?Each of thesequestions presentsome shades ofgray in this worldwe typicallydescribe in blackand white.Change isinevitable. There isa danger in puttingdefinite labels onthings. Doing someans that aschanges take placethese labels passfrom accurate toinaccurate. ReneDescartes thoughtabout change as hepondered a piece ofbeeswax as itmelted in front ofhis fireplace. Atwhat point did thebeeswax changefrom a piece of waxinto a puddle ofwax? At some pointit had to be both asmall puddle and asmall piece of waxat the same time[13]. There is someperiod betweenwhich it is a solidpiece and a purepuddle. Grayness isfuzziness. Einsteinwondered about thegrayness. “So faras the laws ofmathematics referto reality, they arenot certain. And sofar as they arecertain, they do notrefer to reality,” hesaid [13]. Actually,math and sciencedo not fit the worldthey describe. Mathand science areneat and organized.They describe theworld as neat andorganized withoutany grayness.Math and sciencetry to fit everyprocess in theworld to equationsand equations areneat and organized.Imagine a worldwithout grayness.It is impossible.The world we live inis very messy andincludes muchgrayness. Withmath and science,we have observedcertain tendenciesand relationshipsthat have remainedtrue for a period oftime and definedthem asmathematical logicand scientific laws.The truth of thislogic and theselaws is only amatter of degreeand could changeat any moment[13]. They couldpass from accurateto inaccurate atany time. The suncould burn up andnever rise again.The moon couldstop rotatingaround the earth.These neat andorganized laws andrules willexperience change.There is an elementof grayness presenteven in math andscience.To furtherexplain thedifference betweena black and whitescientific ormathematicalmodel compared to2a messy real worldmodel, considerwhen a person turnsfrom a teen to anadult [13]. Figure1.1 shows a graphrepresenting an A-or-not-A approach.It shows that aperson is either anadult or non-adult.Aristotle’sphilosophy wasbased on A-or-not-A.He formulated theLaw of the ExcludedMiddle, which saysthat everything fallsinto either onegroup or the other; itcan’t be in both [8].Figure 1.1:ScientificRepresentationFigure 1.2 shows thesame graph with theshade of grayprinciple, the A-and-not-A principle. Itdoes not followAristotle’s law ofbivalence. Chancesare someone willhave some adultcharacteristics andsome non-adultcharacteristics. Tosome degree theyare an adult and tosome degree theyare not an adult. Figure 2:GraynessRepresentationThisrepresentationseems to mostaccurately describethe world that welive in. However,this idea challengesAristotle and hisphilosophy whichmost of the worldhas embraced for solong. This type ofthinking is againstpresent scientificthought but is keyto fuzzy logic. Grayness is akey idea of fuzzylogic. Fuzzy logic isthe name given tothe analysis thatseeks to define theareas of graynessthat are socharacteristic of theworld we live in.Fuzzy logic is analternative to the A-or-not-A, Boolean 1and 0 logicdefinitions built intosociety. It seeks tohandle the conceptsof partial truth bycreating valuesrepresenting what isbetween total truthand total falsity.Fuzzy logic can beused in almost anyapplication andfocuses onapproximatereasoning whileclassical logic putssuch a largeemphasis exactreasoning. II. HISTORYFuzzy logicbegan in 1965 witha paper called“Fuzzy Sets” by aman named LotfiZadeh. Zadeh is anIranian immigrantand professor fromUC Berkeley’selectricalengineering


View Full Document

CALVIN ENGR 315 - Fuzzy Logic and Fuzzy Control Systems

Documents in this Course
chapter 5

chapter 5

17 pages

lab4

lab4

2 pages

lab 5

lab 5

2 pages

lab 8

lab 8

12 pages

Syllabus

Syllabus

14 pages

lab 2

lab 2

2 pages

chapter-7

chapter-7

13 pages

lab-4

lab-4

2 pages

lab 5

lab 5

2 pages

lab 4

lab 4

2 pages

lab 6

lab 6

2 pages

lab 7

lab 7

3 pages

lab 7

lab 7

3 pages

Syllabus

Syllabus

14 pages

Load more
Download Fuzzy Logic and Fuzzy Control Systems
Our administrator received your request to download this document. We will send you the file to your email shortly.
Loading Unlocking...
Login

Join to view Fuzzy Logic and Fuzzy Control Systems and access 3M+ class-specific study document.

or
We will never post anything without your permission.
Don't have an account?
Sign Up

Join to view Fuzzy Logic and Fuzzy Control Systems 2 2 and access 3M+ class-specific study document.

or

By creating an account you agree to our Privacy Policy and Terms Of Use

Already a member?