What Is Fuzzy Logic?

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2 Tutorial All the information for a given fuzzy inference system is contained in the FIS structure, including variable names, membership function definitions, and so on. This structure can itself be thought of as a hierarchy of structures, as shown in the following diagram. You can generate a listing of information on the FIS using the showfis command, as shown in the following code sample: showfis(a) 1. Name tipper 2. Type mamdani 3. Inputs/Outputs [ 2 1 ] 4. NumInputMFs [ 3 2 ] 5. NumOutputMFs 3 6. NumRules 3 7. AndMethod min 8. OrMethod max 9. ImpMethod min 10. AggMethod max 11. DefuzzMethod centroid 12. InLabels service 2-82
Working from the Command Line 13. food 14. OutLabels tip 15. InRange [ 0 10 ] 16. [ 0 10 ] 17. OutRange [ 0 30 ] 18. InMFLabels poor 19. good 20. excellent 21. rancid 22. delicious 23. OutMFLabels cheap 24. average 25. generous 26. InMFTypes gaussmf 27. gaussmf 28. gaussmf 29. trapmf 30. trapmf 31. OutMFTypes trimf 32. trimf 33. trimf 34. InMFParams [ 1.5 0 0 0 ] 35. [ 1.5 5 0 0 ] 36. [ 1.5 10 0 0 ] 37. [ 0 0 1 3 ] 38. [ 7 9 10 10 ] 39. OutMFParams [ 0 5 10 0 ] 40. [ 10 15 20 0 ] 41. [ 20 25 30 0 ] 42. Rule Antecedent [ 1 1 ] 43. [ 2 0 ] 44. [ 3 2 ] 42. Rule Consequent 1 43. 2 44. 3 42. Rule Weight 1 43. 1 44. 1 42. Rule Connection 2 43. 1 2-83
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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
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Simulink Environment ..............
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x Contents
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1 Getting Started Product Overview
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1 Getting Started If you just want
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1 Getting Started Another basic con
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1 Getting Started Input Space (all
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1 Getting Started do a fine job wit
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1 Getting Started An Introductory E
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1 Getting Started See how the formu
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1 Getting Started tip=(0.15)*servRa
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1 Getting Started 0.25 0.2 tip 0.15
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1 Getting Started or perhaps rewrit
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2 Tutorial Overview The point of fu
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2 Tutorial Foundations of Fuzzy Log
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2 Tutorial How does it work? Reason
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2 Tutorial One of the most commonly
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2 Tutorial A fuzzy set is an extens
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2 Tutorial Polynomial based curves
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2 Tutorial A B min(A,B) A B max(A,B
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2 Tutorial the fuzzy sets to be com
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2 Tutorial if temperature is cold t
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2 Tutorial Fuzzy Inference Systems
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2 Tutorial Information flows from l
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2 Tutorial selects the maximum of t
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2 Tutorial In the following diagram
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2 Tutorial 1. if and then Interpret
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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
Page 108 and 109: 2 Tutorial 25 20 tip 15 10 5 10 8 6
Page 110 and 111: 2 Tutorial a.output(1).mf(2).type='
Page 114 and 115: 2 Tutorial 44. 2 The list of comman
Page 116 and 117: 2 Tutorial Working in Simulink Envi
Page 118 and 119: 2 Tutorial 2 1.8 1.6 1.4 1.2 1 0.8
Page 120 and 121: 2 Tutorial 0.8 0.6 0.4 0.2 valve 0
Page 122 and 123: 2 Tutorial If you stop the simulati
Page 124 and 125: 2 Tutorial FIS. This automatic mode
Page 126 and 127: 2 Tutorial Type slcp attheMATLABpro
Page 128 and 129: 2 Tutorial As the figure shows, the
Page 130 and 131: 2 Tutorial The final output of the
Page 132 and 133: 2 Tutorial the Sugeno-type represen
Page 134 and 135: 2 Tutorial and the membership funct
Page 136 and 137: 2 Tutorial anfis and the ANFIS Edit
Page 138 and 139: 2 Tutorial alone or in combination
Page 140 and 141: 2 Tutorial Constraints of anfis anf
Page 142 and 143: 2 Tutorial You can also load Testin
Page 144 and 145: 2 Tutorial Validating the Trained F
Page 146 and 147: 2 Tutorial The training data appear
Page 148 and 149: 2 Tutorial 1 Choose Grid partition,
Page 150 and 151: 2 Tutorial Viewing Your FIS Structu
Page 152 and 153: 2 Tutorial The plot shows the check
Page 154 and 155: 2 Tutorial • Select the Checking
Page 156 and 157: 2 Tutorial In this case, the checki
Page 158 and 159: 2 Tutorial anfis from the Command L
Page 160 and 161: 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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