What Is Fuzzy Logic?

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2 Tutorial 1. if and then Interpreting the fuzzy inference diagram 2. if and then input 1 input 2 output In this figure, the flow proceeds up from the inputs in the lower left, then across each row, or rule, and then down the rule outputs to finish in the lower right. This compact flow shows everything at once, from linguistic variable fuzzification all the way through defuzzification of the aggregate output. 2-28
<strong>Fuzzy</strong> Inference Systems The following figure shows the actual full-size fuzzy inference diagram. There is a lot to see in a fuzzy inference diagram, but after you become accustomed to it, you can learn a lot about a system very quickly. For instance, from this diagram with these particular inputs, you can easily see that the implication method is truncation with the min function. The max function is being used for the fuzzy OR operation. Rule 3 (the bottom-most row in the diagram shown previously) is having the strongest influence on the output. and so on. TheRuleViewerdescribedin“TheRuleViewer”onpage2-53isaMATLAB implementation of the fuzzy inference diagram. 2-29
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Fuzzy Logic Toolbox 2 User’s Guid
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Revision History January 1995 First
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Overview of Fuzzy Inference Process
Page 8 and 9: Simulink Environment ..............
Page 10 and 11: x Contents
Page 12 and 13: 1 Getting Started Product Overview
Page 14 and 15: 1 Getting Started If you just want
Page 16 and 17: 1 Getting Started Another basic con
Page 18 and 19: 1 Getting Started Input Space (all
Page 20 and 21: 1 Getting Started do a fine job wit
Page 22 and 23: 1 Getting Started An Introductory E
Page 24 and 25: 1 Getting Started See how the formu
Page 26 and 27: 1 Getting Started tip=(0.15)*servRa
Page 28 and 29: 1 Getting Started 0.25 0.2 tip 0.15
Page 30 and 31: 1 Getting Started or perhaps rewrit
Page 32 and 33: 2 Tutorial Overview The point of fu
Page 34 and 35: 2 Tutorial Foundations of Fuzzy Log
Page 36 and 37: 2 Tutorial How does it work? Reason
Page 38 and 39: 2 Tutorial One of the most commonly
Page 40 and 41: 2 Tutorial A fuzzy set is an extens
Page 42 and 43: 2 Tutorial Polynomial based curves
Page 44 and 45: 2 Tutorial A B min(A,B) A B max(A,B
Page 46 and 47: 2 Tutorial the fuzzy sets to be com
Page 48 and 49: 2 Tutorial if temperature is cold t
Page 50 and 51: 2 Tutorial Fuzzy Inference Systems
Page 52 and 53: 2 Tutorial Information flows from l
Page 54 and 55: 2 Tutorial selects the maximum of t
Page 56 and 57: 2 Tutorial In the following diagram
Page 60 and 61: 2 Tutorial Customization Oneofthepr
Page 62 and 63: 2 Tutorial adaptive neural fuzzy in
Page 64 and 65: 2 Tutorial Getting Started We’ll
Page 66 and 67: 2 Tutorial Name of the system. Chan
Page 68 and 69: 2 Tutorial 8 Select File > Export >
Page 70 and 71: 2 Tutorial Editor shares some featu
Page 72 and 73: 2 Tutorial can first use the mouse
Page 74 and 75: 2 Tutorial d Verify that 3 is selec
Page 76 and 77: 2 Tutorial 5 In the FIS Variables a
Page 78 and 79: 2 Tutorial Now that the variables h
Page 80 and 81: 2 Tutorial one of the variable qual
Page 82 and 83: 2 Tutorial A literal interpretation
Page 84 and 85: 2 Tutorial Notice that under food,
Page 86 and 87: 2 Tutorial Upon opening the Surface
Page 88 and 89: 2 Tutorial Building Fuzzy Inference
Page 90 and 91: 2 Tutorial Specifying Custom Member
Page 92 and 93: 2 Tutorial 3 In the FIS Editor, sel
Page 94 and 95: 2 Tutorial Using the custmf1 exampl
Page 96 and 97: 2 Tutorial 6 To add custom membersh
Page 98 and 99: 2 Tutorial In the toolbox, the AND
Page 100 and 101: 2 Tutorial 1 In the lower-left pane
Page 102 and 103: 2 Tutorial Working from the Command
Page 104 and 105: 2 Tutorial The structure.field synt
Page 106 and 107: 2 Tutorial After closing any open M
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2 Tutorial 25 20 tip 15 10 5 10 8 6
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2 Tutorial a.output(1).mf(2).type='
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2 Tutorial All the information for
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2 Tutorial 44. 2 The list of comman
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2 Tutorial Working in Simulink Envi
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2 Tutorial 2 1.8 1.6 1.4 1.2 1 0.8
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2 Tutorial 0.8 0.6 0.4 0.2 valve 0
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2 Tutorial If you stop the simulati
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2 Tutorial FIS. This automatic mode
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2 Tutorial Type slcp attheMATLABpro
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2 Tutorial As the figure shows, the
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2 Tutorial The final output of the
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2 Tutorial the Sugeno-type represen
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2 Tutorial and the membership funct
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2 Tutorial anfis and the ANFIS Edit
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2 Tutorial alone or in combination
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2 Tutorial Constraints of anfis anf
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2 Tutorial You can also load Testin
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2 Tutorial Validating the Trained F
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2 Tutorial The training data appear
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2 Tutorial 1 Choose Grid partition,
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2 Tutorial Viewing Your FIS Structu
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2 Tutorial The plot shows the check
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2 Tutorial • Select the Checking
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2 Tutorial In this case, the checki
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2 Tutorial anfis from the Command L
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2 Tutorial data values. You use the
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2 Tutorial subplot(2,2,4) plotmf(fi
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2 Tutorial Thedifferencebetweentheo
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2 Tutorial As a result, the trainin
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2 Tutorial Output FIS Structure for
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2 Tutorial Output FIS Structure for
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2 Tutorial right location within a
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2 Tutorial Here, the variable cente
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2 Tutorial Finally, plot the two cl
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2 Tutorial The genfis2 function bui
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2 Tutorial fismat=genfis2(datin,dat
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2 Tutorial At this point, you can u
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2 Tutorial The next figure shows th
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2 Tutorial you fit the fuzzy system
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2 Tutorial Plot the checking error
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2 Tutorial information on the clust
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2 Tutorial Saving the Cluster Cente
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2 Tutorial The fis.c file contains
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2 Tutorial This command saves the i
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2 Tutorial 'lcclnk' is not recogniz
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2 Tutorial 7 To verify that the fuz
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3 Function Reference GUI Tools and
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3 Function Reference FIS Data Struc
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3 Function Reference Simulink Envir
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addmf Purpose Syntax Description Ad
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addrule Purpose Syntax Description
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anfis Purpose Syntax Description Tr
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anfis training data set. When you s
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anfisedit Purpose Syntax Descriptio
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defuzz Purpose Syntax Description D
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dsigmf See Also gaussmf, gauss2mf,
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evalfis • ORR: the result of eval
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fcm Purpose Syntax Description Fuzz
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findcluster Purpose Syntax Descript
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fuzarith Purpose Syntax Description
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fuzzy Purpose Syntax Open basic Fuz
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fuzzy Membership functions to invok
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gauss2mf See Also dsigmf, gauss2mf,
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gbellmf Purpose Syntax Description
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genfis1 The default number of membe
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genfis2 Purpose Syntax Description
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genfis2 Xin1 = 7*rand(50,1); Xin2 =
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genfis2 Example 3 The following exa
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genfis3 The input membership functi
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genfis3 Example 3 The following exa
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gensurf Example 1 a = readfis('tipp
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getfis 'inlabels' 'outlabels' 'inra
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getfis getfis(a,'input',1) Name = s
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mam2sug Purpose Syntax Description
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mf2mf 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3
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mfedit Selecttheiconforthevariableo
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newfis DefuzzMethod ans = [newsys]
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pimf Purpose Syntax Description Exa
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plotmf Purpose Syntax Description E
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psigmf Purpose Syntax Description B
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mmf Purpose Syntax Description Remo
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mvar Purpose Syntax Description Rem
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uleedit Purpose Syntax Description
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uleview Purpose Syntax Rule viewer
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setfis Purpose Syntax Description S
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sffis Purpose Syntax Description Fu
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showfis 24. average 25. generous 26
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showrule 1. (service==poor) | (food
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sigmf See Also dsigmf, gaussmf, gau
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subclust Purpose Syntax Description
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subclust Examples [C,S] = subclust(
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surfview If you want to create a sm
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trapmf Purpose Syntax Description T
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trimf Purpose Syntax Description Tr
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writefis Purpose Syntax Description
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zmf See Also dsigmf, gaussmf, gauss
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5 Block Reference Controllers Fuzzy
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5 Block Reference Membership Functi
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Diff. Sigmoidal MF Purpose Descript
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Fuzzy Logic Controller with Rulevie
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Gaussian2 MF Purpose Description Co
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Pi-shaped MF Purpose Description Se
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Probabilistic Rule Agg Purpose Desc
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S-shaped MF Purpose Description See
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Trapezoidal MF Purpose Description
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Z-shaped MF Purpose Description See
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A Examples Introductory Examples
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B Bibliography [1] Bezdec, J.C., Pa
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B Bibliography [22] Zadeh, L.A.,
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Glossary fuzzy c-means clustering A
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Glossary Glossary-4
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Index H hybrid method 2-121 I if-th
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What Is Fuzzy Logic?

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