What Is Fuzzy Logic?

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2 Tutorial adaptive neural fuzzy inference systems. The ANFIS Editor GUI is discussed in “Sugeno-Type Fuzzy Inference” on page 2-99. FIS Editor Rule Editor Membership Function Editor Fuzzy Inference System Read-only tools Rule Viewer Surface Viewer The FIS Editor handles the high-level issues for the system: How many input and output variables? What are their names? Fuzzy Logic Toolbox software does not limit the number of inputs. However, the number of inputs may be limited by the available memory of your machine. If the number of inputs is too large, or the number of membership functions is too big, then it may also be difficult to analyze the FIS using the other GUI tools. The Membership Function Editor is used to define the shapes of all the membership functions associated with each variable. The Rule Editor is for editing the list of rules that defines the behavior of the system. 2-32
Building Systems with Fuzzy Logic Toolbox Software The Rule Viewer and the Surface Viewer are used for looking at, as opposed to editing, the FIS. They are strictly read-only tools. The Rule Viewer is a MATLAB technical computing environment based display of the fuzzy inference diagram shown at the end of the last section. Used as a diagnostic, it can show (for example) which rules are active, or how individual membership function shapes are influencing the results. The Surface Viewer is used to display the dependency of one of the outputs on any one or two of the inputs—that is, it generates and plots an output surface map for the system. This section began with an illustration similar to the following one describing the main parts of a fuzzy inference system, only the next one shows how the three editors fit together. The two viewers examine the behavior of the entire system. The General Case... A Specific Example... The GUI Editors... Input Output service tip The FIS Editor Rules if service is poor then tip is cheap if service is good then tip is average if service is excellent then tip is generous The Rule Editor Input terms (interpret) Output terms (assign) service = tip = {poor, good, excellent} {cheap, average, generous} The Membership Function Editor The five primary GUIs can all interact and exchange information. Any one of them can read and write both to theworkspaceandtoafile (the read-only viewers can still exchange plots with the workspace and save them to a file). For any fuzzy inference system, any or all of these five GUIs may be open. If more than one of these editors is open for a single system, the various GUI windows are aware of the existence of the others, and, if necessary, updates related windows. Thus, if the names of the membership functions are changed using the Membership Function Editor, those changes are reflected in the rules shown in the Rule Editor. The editors for any number of different FIS systems may be open simultaneously. The FIS Editor, the Membership Function Editor, and the Rule Editor can all read and modify the FIS data, but the Rule Viewer and the Surface Viewer do not modify the FIS data in any way. 2-33
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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
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 58 and 59: 2 Tutorial 1. if and then Interpret
Page 60 and 61: 2 Tutorial Customization Oneofthepr
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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Page 110 and 111: 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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