What Is Fuzzy Logic?

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addrule Purpose Syntax Description Examples Add rule to <strong>Fuzzy</strong> Inference System a = addrule(a,ruleList) addrule has two arguments. The first argument is the MATLAB workspace variable FIS name. The second argument is a matrix of one or more rows, each of which represents a given rule. The format that the rule list matrix must take is very specific. If there are m inputs to a system and n outputs, there must be exactly m + n +2columns to the rule list. The first m columns refer to the inputs of the system. Each column contains a number that refers to the index of the membership function for that variable. The next n columns refer to the outputs of the system. Each column contains a number that refers to the index of the membership function for that variable. The m + n + 1 column contains the weight that is to be applied to therule. Theweightmustbeanumberbetweenzeroandoneandis generally left as one. The m + n + 2 column contains a 1 if the fuzzy operator for the rule’s antecedent is AND. It contains a 2 if the fuzzy operator is OR. ruleList=[ 1 1 1 1 1 1 2 2 1 1]; a = addrule(a,ruleList); If the system a has two inputs and one output, the first rule can be interpreted as: “If Input 1 is MF 1 and Input 2 is MF 1, then Output 1 is MF 1.” See Also addmf, addvar, parsrule, rmmf, rmvar, showrule 4-4
addvar Purpose Syntax Description Addvariableto<strong>Fuzzy</strong>InferenceSystem a = addvar(a,'varType','varName',varBounds) addvar has four arguments in this order: • The name of a FIS structure in the MATLAB workspace • A string representing the type ofthevariableyouwanttoadd ('input' or 'output') • A string representing the name of the variable you want to add • The vector describing the limiting range values for the variable you want to add Indices are applied to variables in the order in which they are added, so the first input variable added to a system is always known as input variable number one for that system. Input and output variables are numbered independently. Examples a = newfis('tipper'); a = addvar(a,'input','service',[0 10]); getfis(a,'input',1) This command returns the following result: Name = service NumMFs = 0 MFLabels = Range = [0 10] See Also addmf, addrule, rmmf, rmvar 4-5
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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
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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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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
Page 194 and 195: 2 Tutorial The fis.c file contains
Page 196 and 197: 2 Tutorial This command saves the i
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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 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,
Page 222 and 223: evalfis • ORR: the result of eval
Page 224 and 225: fcm Purpose Syntax Description Fuzz
Page 226 and 227: findcluster Purpose Syntax Descript
Page 228 and 229: fuzarith Purpose Syntax Description
Page 230 and 231: fuzzy Purpose Syntax Open basic Fuz
Page 232 and 233: fuzzy Membership functions to invok
Page 234 and 235: gauss2mf See Also dsigmf, gauss2mf,
Page 236 and 237: gbellmf Purpose Syntax Description
Page 238 and 239: genfis1 The default number of membe
Page 240 and 241: genfis2 Purpose Syntax Description
Page 242 and 243: genfis2 Xin1 = 7*rand(50,1); Xin2 =
Page 244 and 245: genfis2 Example 3 The following exa
Page 246 and 247: genfis3 The input membership functi
Page 248 and 249: genfis3 Example 3 The following exa
Page 250 and 251: gensurf Example 1 a = readfis('tipp
Page 252 and 253: getfis 'inlabels' 'outlabels' 'inra
Page 254 and 255: getfis getfis(a,'input',1) Name = s
Page 256 and 257: mam2sug Purpose Syntax Description
Page 258 and 259: 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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