What Is Fuzzy Logic?

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Index H hybrid method 2-121 I if-then rules 2-17 antecedent 2-16 implication 2-17 implication 2-17 See also if-then rules initfcm 2-144 L logical operations 2-13 M mam2sug 4-50 Mamdani-style inference Glossary-2 Mamdani-type inference 2-19 max 2-25 membership function 2-7 mf editor 2-120 Membership Function Editor 2-31 membership functions bell 2-11 custom 2-60 Gaussian 2-11 Pi 2-12 S 2-12 sigmoidal 2-11 Z 2-12 MF. Seemembership function mf2mf 4-51 mfedit 4-53 min 2-29 model validation 2-108 N neuro-fuzzy inference 2-107 newfis 2-80 NOT 2-15 O OR 2-15 P pimf 2-12 plotfis 2-75 plotmf 2-76 probabilistic OR 2-23 probor 2-25 psigmf 2-11 R readfis 2-72 rmmf 2-84 rmvar 2-84 Rule Editor 2-31 rule formats 4-10 Rule Viewer 2-31 ruleedit 4-68 ruleview 4-70 S setfis 2-74 sffis 4-74 showfis 2-74 showrule 4-77 sigmf 2-11 Simulink blocks fuzzy controller with ruleviewer 2-90 <strong>Fuzzy</strong> <strong>Logic</strong> Controller 2-87 Simulink, working with 2-86 singleton 2-21 Index-2
Index sltank 2-87 smf 2-12 stand-alone C code 2-163 stand-alone fuzzy inference engine 2-163 step size 2-138 structure.field syntax 2-74 subclust 4-82 subtractive clustering 2-147 Sugeno 2-148 Sugeno-type FIS 2-118 See also Sugeno-type inference Sugeno-type inference 2-21 sum 2-25 Surface Viewer 2-31 surfview 4-85 T T-conorm 2-16 T-norm 2-15 testing data 2-109 training data 2-109 training error 2-138 trapezoidal 2-10 trapmf 2-10 trimf 4-90 U UNIX stand-alone fuzzy inference engine 2-168 W Windows stand-alone fuzzy inference engine 2-164 writefis 4-92 Z zmf 2-12 Index-3
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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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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
Page 282 and 283: showfis 24. average 25. generous 26
Page 284 and 285: showrule 1. (service==poor) | (food
Page 286 and 287: sigmf See Also dsigmf, gaussmf, gau
Page 288 and 289: subclust Purpose Syntax Description
Page 290 and 291: subclust Examples [C,S] = subclust(
Page 292 and 293: surfview If you want to create a sm
Page 294 and 295: trapmf Purpose Syntax Description T
Page 296 and 297: trimf Purpose Syntax Description Tr
Page 298 and 299: writefis Purpose Syntax Description
Page 300 and 301: zmf See Also dsigmf, gaussmf, gauss
Page 302 and 303: 5 Block Reference Controllers Fuzzy
Page 304 and 305: 5 Block Reference Membership Functi
Page 306 and 307: Diff. Sigmoidal MF Purpose Descript
Page 308 and 309: Fuzzy Logic Controller with Rulevie
Page 310 and 311: Gaussian2 MF Purpose Description Co
Page 312 and 313: Pi-shaped MF Purpose Description Se
Page 314 and 315: Probabilistic Rule Agg Purpose Desc
Page 316 and 317: S-shaped MF Purpose Description See
Page 318 and 319: Trapezoidal MF Purpose Description
Page 320 and 321: Z-shaped MF Purpose Description See
Page 322 and 323: A Examples Introductory Examples
Page 324 and 325: B Bibliography [1] Bezdec, J.C., Pa
Page 326 and 327: B Bibliography [22] Zadeh, L.A.,
Page 328 and 329: Glossary fuzzy c-means clustering A
Page 330 and 331: Glossary Glossary-4
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What Is Fuzzy Logic?

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