What Is Fuzzy Logic?

More documents

Recommendations

Info

2 Tutorial 5 In the FIS Variables area, click the input variable food to select it. 6 Enter [0 10] in the Range and the Display Range fields. 7 Create the membership functions for the input variable food. a Select Edit > Remove All MFs to remove the default Membership Functions for the input variable food. b Select Edit > Add MFs to open the Membership Functions dialog box. c In the Membership Functions dialog box, select trapmf as the MF Type. d Select 2 in the Number of MFs drop-down list. e Click OK to add two trapezoidal curves to the input variable food. 2-46
Building Systems with <strong>Fuzzy</strong> <strong>Logic</strong> Toolbox Software 8 Rename the membership functions for the input variable food, andspecify their parameters: a In the FIS Variables area, click the input variable food to select it. b Click on the curve named mf1, and specify the following fields in the Current Membership Function (click on MF to select) area: c • In the Name field, enter rancid. • In the Params field, enter [0 0 1 3]. Click on the curve named mf2 to select it, and enter delicious in the Name field. Reset the associated parameters if desired. 9 Click on the output variable tip to select it. 10 Enter [0 30] in the Range and the Display Range fields to cover the output range. The inputs ranges from 0 to 10, but the output is a tip between 5% and 25%. 11 Rename the default triangular membership functions for the output variable tip, and specify their parameters. a Click the curve named mf1 to select it, and specify the following fields in the Current Membership Function (click on MF to select) area: • In the Name field, enter cheap. • In the Params field, enter [0 5 10]. b Click the curve named mf2 to select it, and specify the following fields in the Current Membership Function (click on MF to select) area: c • In the Name field, enter average. • In the Params field, enter [10 15 20]. Click the curve named mf3 to select it, and specify the following: • In the Name field, enter generous. • In the Params field, enter [20 25 30]. The Membership Function Editor looks similar to the following figure. 2-47
Page 1 and 2:
Fuzzy Logic Toolbox 2 User’s Guid
Page 3:
Revision History January 1995 First
Page 6 and 7:
Overview of Fuzzy Inference Process
Page 8 and 9:
Simulink Environment ..............
Page 10 and 11:
x Contents
Page 12 and 13:
1 Getting Started Product Overview
Page 14 and 15:
1 Getting Started If you just want
Page 16 and 17:
1 Getting Started Another basic con
Page 18 and 19:
1 Getting Started Input Space (all
Page 20 and 21:
1 Getting Started do a fine job wit
Page 22 and 23:
1 Getting Started An Introductory E
Page 24 and 25:
1 Getting Started See how the formu
Page 26 and 27: 1 Getting Started tip=(0.15)*servRa
Page 28 and 29: 1 Getting Started 0.25 0.2 tip 0.15
Page 30 and 31: 1 Getting Started or perhaps rewrit
Page 32 and 33: 2 Tutorial Overview The point of fu
Page 34 and 35: 2 Tutorial Foundations of Fuzzy Log
Page 36 and 37: 2 Tutorial How does it work? Reason
Page 38 and 39: 2 Tutorial One of the most commonly
Page 40 and 41: 2 Tutorial A fuzzy set is an extens
Page 42 and 43: 2 Tutorial Polynomial based curves
Page 44 and 45: 2 Tutorial A B min(A,B) A B max(A,B
Page 46 and 47: 2 Tutorial the fuzzy sets to be com
Page 48 and 49: 2 Tutorial if temperature is cold t
Page 50 and 51: 2 Tutorial Fuzzy Inference Systems
Page 52 and 53: 2 Tutorial Information flows from l
Page 54 and 55: 2 Tutorial selects the maximum of t
Page 56 and 57: 2 Tutorial In the following diagram
Page 58 and 59: 2 Tutorial 1. if and then Interpret
Page 60 and 61: 2 Tutorial Customization Oneofthepr
Page 62 and 63: 2 Tutorial adaptive neural fuzzy in
Page 64 and 65: 2 Tutorial Getting Started We’ll
Page 66 and 67: 2 Tutorial Name of the system. Chan
Page 68 and 69: 2 Tutorial 8 Select File > Export >
Page 70 and 71: 2 Tutorial Editor shares some featu
Page 72 and 73: 2 Tutorial can first use the mouse
Page 74 and 75: 2 Tutorial d Verify that 3 is selec
Page 78 and 79: 2 Tutorial Now that the variables h
Page 80 and 81: 2 Tutorial one of the variable qual
Page 82 and 83: 2 Tutorial A literal interpretation
Page 84 and 85: 2 Tutorial Notice that under food,
Page 86 and 87: 2 Tutorial Upon opening the Surface
Page 88 and 89: 2 Tutorial Building Fuzzy Inference
Page 90 and 91: 2 Tutorial Specifying Custom Member
Page 92 and 93: 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
Page 108 and 109: 2 Tutorial 25 20 tip 15 10 5 10 8 6
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
Page 118 and 119: 2 Tutorial 2 1.8 1.6 1.4 1.2 1 0.8
Page 120 and 121: 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 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
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,
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
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 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
Page 332 and 333:
Index H hybrid method 2-121 I if-th
show all

What Is Fuzzy Logic?

Create successful ePaper yourself

Delete template?

Save as template?