What Is Fuzzy Logic?

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2 Tutorial Validating the Trained FIS After the FIS is trained, validate the model using a Testing or Checking data that differs from the one you used to train the FIS. To validate the trained FIS: 1 Select the validation data set and click Load Data. 2 Click Test Now. This action plots the test data against the FIS output (shown in red) in the plot. For more information on the use of testing data and checking data for model validation, see “Model Validation Using Testing and Checking Data Sets” on page 2-109. ANFIS Editor GUI Example 1: Checking Data Helps Model Validation In this section we look at an example that loads similar training and checking data sets. The checking data set is corrupted by noise. Loading Data To work both of the following examples, you load the training data sets (fuzex1trnData and fuzex2trnData) andthecheckingdatasets (fuzex1chkData and fuzex2chkData), into the ANFIS Editor GUI from the workspace. You may also substitute your own data sets. To load the data sets from the workspaceintotheANFISEditorGUI: 1 Type the following commands at the MATLAB command line to load the data sets from the directory fuzzydemos into the MATLAB workspace: load fuzex1trnData.dat load fuzex2trnData.dat load fuzex1chkData.dat load fuzex2chkData.dat 2-114
anfis and the ANFIS Editor GUI 2 Open the ANFIS Editor GUI by typing anfisedit in the MATLAB command line. 3 To load the training data set from the workspace, select Type: Training, From: worksp. in the Load data portion of the ANFIS Editor GUI. Then, click the Load Data button. The small GUI window that opens allowsyoutotypeinavariablename from the workspace. Type in fuzex1trnData asshowninthefollowing figure, and click OK. 2-115
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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
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
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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 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
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
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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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