What Is Fuzzy Logic?

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2 Tutorial right location within a data set. This iteration is based on minimizing an objective function that represents the distance from any given data point to a cluster center weighted by that data point’s membership grade. The command line function fcm outputs a list of cluster centers and several membership grades for each data point. You can use the information returned by fcm to help you build a fuzzy inference system by creating membership functions to represent the fuzzy qualities of each cluster. 2-142
<strong>Fuzzy</strong> Clustering An Example: 2-D Clusters You can use quasi-random two-dimensional data to illustrate how FCM clustering works. To load the data set and plot it, type the following commands: load fcmdata.dat plot(fcmdata(:,1),fcmdata(:,2),'o') Next, invoke the command-line function fcm to find two clusters in this data set until the objective function is no longer decreasing much at all. [center,U,objFcn] = fcm(fcmdata,2); 2-143
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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
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2 Tutorial Now that the variables h
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2 Tutorial one of the variable qual
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2 Tutorial A literal interpretation
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2 Tutorial Notice that under food,
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2 Tutorial Upon opening the Surface
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2 Tutorial Building Fuzzy Inference
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2 Tutorial Specifying Custom Member
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2 Tutorial 3 In the FIS Editor, sel
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2 Tutorial Using the custmf1 exampl
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2 Tutorial 6 To add custom membersh
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2 Tutorial In the toolbox, the AND
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2 Tutorial 1 In the lower-left pane
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2 Tutorial Working from the Command
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2 Tutorial The structure.field synt
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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
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
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 174 and 175: 2 Tutorial Here, the variable cente
Page 176 and 177: 2 Tutorial Finally, plot the two cl
Page 178 and 179: 2 Tutorial The genfis2 function bui
Page 180 and 181: 2 Tutorial fismat=genfis2(datin,dat
Page 182 and 183: 2 Tutorial At this point, you can u
Page 184 and 185: 2 Tutorial The next figure shows th
Page 186 and 187: 2 Tutorial you fit the fuzzy system
Page 188 and 189: 2 Tutorial Plot the checking error
Page 190 and 191: 2 Tutorial information on the clust
Page 192 and 193: 2 Tutorial Saving the Cluster Cente
Page 194 and 195: 2 Tutorial The fis.c file contains
Page 196 and 197: 2 Tutorial This command saves the i
Page 198 and 199: 2 Tutorial 'lcclnk' is not recogniz
Page 200 and 201: 2 Tutorial 7 To verify that the fuz
Page 202 and 203: 3 Function Reference GUI Tools and
Page 204 and 205: 3 Function Reference FIS Data Struc
Page 206 and 207: 3 Function Reference Simulink Envir
Page 208 and 209: addmf Purpose Syntax Description Ad
Page 210 and 211: addrule Purpose Syntax Description
Page 212 and 213: anfis Purpose Syntax Description Tr
Page 214 and 215: anfis training data set. When you s
Page 216 and 217: anfisedit Purpose Syntax Descriptio
Page 218 and 219: defuzz Purpose Syntax Description D
Page 220 and 221: 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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