What Is Fuzzy Logic?

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2 Tutorial As the figure shows, the Fuzzy Logic Controller block uses built-in Simulink blocks to implement your FIS. Although the models can grow complex, this representation is better suited than the S-function sffis for efficient code generation. 2-98
Sugeno-Type Fuzzy Inference Sugeno-Type Fuzzy Inference In this section... “What is Sugeno-Type Fuzzy Inference?” on page 2-99 “An Example: Two Lines” on page 2-103 “Comparison of Sugeno and Mamdani Methods” on page 2-105 What is Sugeno-Type Fuzzy Inference? The fuzzy inference process discussed so far is Mamdani’s fuzzy inference method, the most common methodology. This section discusses the so-called Sugeno, or Takagi-Sugeno-Kang, method of fuzzy inference. Introduced in 1985 [16], it is similar to the Mamdani method in many respects. The first two parts of the fuzzy inference process, fuzzifying the inputs and applying the fuzzy operator, are exactly the same. The main difference between Mamdani and Sugeno is that the Sugeno output membership functions are either linear or constant. A typical rule in a Sugeno fuzzy model has the form If Input 1 = x and Input 2 = y, thenOutputisz=ax+by+c For a zero-order Sugeno model, the output level z is a constant (a=b =0). The output level z i of each rule is weighted by the firing strength w i of the rule. For example, for an AND rule with Input 1 = x and Input 2 = y, the firing strength is w i = AndMethod (F 1 (x), F 2 (y)) where F 1,2 (.) are the membership functions for Inputs 1 and 2. 2-99
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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
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2 Tutorial adaptive neural fuzzy in
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2 Tutorial Getting Started We’ll
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2 Tutorial Name of the system. Chan
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2 Tutorial 8 Select File > Export >
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2 Tutorial Editor shares some featu
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2 Tutorial can first use the mouse
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2 Tutorial d Verify that 3 is selec
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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='
Page 112 and 113: 2 Tutorial All the information for
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 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
Page 162 and 163: 2 Tutorial subplot(2,2,4) plotmf(fi
Page 164 and 165: 2 Tutorial Thedifferencebetweentheo
Page 166 and 167: 2 Tutorial As a result, the trainin
Page 168 and 169: 2 Tutorial Output FIS Structure for
Page 170 and 171: 2 Tutorial Output FIS Structure for
Page 172 and 173: 2 Tutorial right location within a
Page 174 and 175: 2 Tutorial Here, the variable cente
Page 176 and 177: 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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