What Is Fuzzy Logic?

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subclust Examples [C,S] = subclust(X,0.5) This command sets the minimum number of arguments needed to use this function. A range of influence of 0.5 has been specified for all data dimensions. [C,S] = subclust(X,[0.5 0.25 0.3],[],[2.0 0.8 0.7]) This command assumes the data dimension is 3 (X has 3 columns) and uses a range of influence of 0.5, 0.25, and 0.3 for the first, second, and third data dimension, respectively. The scaling factors for mapping the data into a unit hyperbox are obtained from the minimum and maximum data values. The squashFactor is set to 2.0, indicating that you only want to find clusters that are far from each other. The acceptRatio is set to 0.8, indicating that only data points that have a very strong potential for being cluster centers are accepted. The rejectRatio is set to 0.7, indicating that you want to reject all data points without a strong potential. References See Also Chiu, S., "Fuzzy Model Identification Based on Cluster Estimation," Journal of Intelligent & Fuzzy Systems, Vol.2,No.3,Sept.1994. Yager, R. and D. Filev, "Generation of Fuzzy Rules by Mountain Clustering," Journal of Intelligent & Fuzzy Systems, Vol. 2, No. 3, pp. 209-219, 1994. genfis2 4-84
surfview Purpose Syntax Open Output Surface Viewer surfview('a') Description The Surface Viewer, invoked using surfview('a'), isaGUItoolthat lets you examine the output surface of a FIS stored in a file, a.fis, for any one or two inputs. Because it does not alter the fuzzy system or its associated FIS structure in any way, Surface Viewer is a read-only editor. Using the drop-down menus, you select the two input variables you want assigned to the two input axes (X and Y), as well the output variable you want assigned to the output (or Z) axis. 4-85
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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
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
Page 260 and 261: mfedit Selecttheiconforthevariableo
Page 262 and 263: newfis DefuzzMethod ans = [newsys]
Page 264 and 265: pimf Purpose Syntax Description Exa
Page 266 and 267: plotmf Purpose Syntax Description E
Page 268 and 269: psigmf Purpose Syntax Description B
Page 270 and 271: mmf Purpose Syntax Description Remo
Page 272 and 273: mvar Purpose Syntax Description Rem
Page 274 and 275: uleedit Purpose Syntax Description
Page 276 and 277: uleview Purpose Syntax Rule viewer
Page 278 and 279: setfis Purpose Syntax Description S
Page 280 and 281: 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 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
Page 332 and 333: Index H hybrid method 2-121 I if-th
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What Is Fuzzy Logic?

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