AI - a Guide to Intelligent Systems.pdf - Member of EEPIS

More documents

Recommendations

Info

WILL A NEURAL NETWORK WORK FOR MY PROBLEM? 325 Another problem is overfitting. The greater the number of hidden neurons, the greater the ability of the network to recognise existing patterns. However, if the number of hidden neurons is too big, the network might simply memorise all training examples. This may prevent it from generalising, or producing correct outputs when presented with data that was not used in training. For instance, the overfitted character recognition network trained with Helvetica-font examples might not be able to recognise the same characters in the Times New Roman font. The practical approach to preventing overfitting is to choose the smallest number of hidden neurons that yields good generalisation. Thus, at the starting point, an experimental study could begin with as little as two neurons in the hidden layer. In our example, we will examine the system’s performance with 2, 5, 10 and 20 hidden neurons and compare results. The architecture of a neural network (with five neurons in the hidden layer) for the character recognition problem is shown in Figure 9.20. Neurons in the hidden and output layers use a sigmoid activation function. The neural network is trained with the back-propagation algorithm with momentum; the momentum constant is set to 0.95. The input and output training patterns are shown in Table 9.2. The binary input vectors representing the bit maps of the respective digits are fed directly into the network. The network’s performance in our study is measured by the sum of squared errors. Figure 9.21 demonstrates the results; as can be seen from Figure 9.21(a), a neural network with two neurons in the hidden layer cannot converge to a solution, while the networks with 5, 10 and 20 hidden neurons learn relatively fast. In fact, they converge in less than 250 epochs (each epoch represents an entire pass through all training examples). Also note that the network with 20 hidden neurons shows the fastest convergence. Once the training is complete, we must test the network with a set of test examples to see how well it performs. Figure 9.20 Neural network for printed digit recognition
326 KNOWLEDGE ENGINEERING AND DATA MINING Table 9.2 Input and desired output patterns for the digit recognition neural network Input patterns Rows in the pixel matrix Desired output Digit 1 2 3 4 5 6 7 8 9 patterns 1 00100 01100 10100 00100 00100 00100 00100 00100 00100 1000000000 2 01110 10001 00001 00001 00010 00100 01000 10000 11111 0100000000 3 01110 10001 00001 00001 00010 00001 00001 10001 01110 0010000000 4 00010 00110 00110 01010 01010 10010 11111 00010 00010 0001000000 5 11111 10000 10000 11110 10001 00001 00001 10001 01110 0000100000 6 01110 10001 10000 10000 11110 10001 10001 10001 01110 0000010000 7 11111 00001 00010 00010 00100 00100 01000 01000 01000 0000001000 8 01110 10001 10001 10001 01110 10001 10001 10001 01110 0000000100 9 01110 10001 10001 10001 01111 00001 00001 10001 01110 0000000010 0 01110 10001 10001 10001 10001 10001 10001 10001 01110 0000000001
Page 1 and 2:
MICHAEL NEGNEVITSKY Artificial Inte
Page 3 and 4:
We work with leading authors to dev
Page 5 and 6:
Pearson Education Limited Edinburgh
Page 8 and 9:
Contents Preface Preface to the sec
Page 10 and 11:
CONTENTS ix 7 Evolutionary computat
Page 12 and 13:
Preface ‘The only way not to succ
Page 14 and 15:
PREFACE xiii In Chapter 3, we prese
Page 16:
Prefacetothesecondedition The main
Page 20 and 21:
Introduction to knowledgebased inte
Page 22 and 23:
INTELLIGENT MACHINES 3 Figure 1.1 T
Page 24 and 25:
THE HISTORY OF ARTIFICIAL INTELLIGE
Page 26 and 27:
Page 28 and 29:
Page 30 and 31:
Page 32 and 33:
Page 34 and 35:
Page 36 and 37:
SUMMARY 17 Although fuzzy systems a
Page 38 and 39:
SUMMARY 19 Table 1.1 Period A summa
Page 40 and 41:
QUESTIONS FOR REVIEW 21 or fuzzy se
Page 42 and 43:
REFERENCES 23 Kosko, B. (1997). Fuz
Page 44 and 45:
Rule-based expert systems 2 In whic
Page 46 and 47:
RULES AS A KNOWLEDGE REPRESENTATION
Page 48 and 49:
THE MAIN PLAYERS IN THE EXPERT SYST
Page 50 and 51:
STRUCTURE OF A RULE-BASED EXPERT SY
Page 52 and 53:
FUNDAMENTAL CHARACTERISTICS OF AN E
Page 54 and 55:
FORWARD AND BACKWARD CHAINING INFER
Page 56 and 57:
Page 58 and 59:
Page 60 and 61:
MEDIA ADVISOR: A DEMONSTRATION RULE
Page 62 and 63:
MEDIA ADVISOR: A DEMONSTRATION RULE
Page 64 and 65:
MEDIA: A DEMONSTRATION RULE-BASED E
Page 66 and 67:
CONFLICT RESOLUTION 47 Does MEDIA A
Page 68 and 69:
CONFLICT RESOLUTION 49 . Fire the m
Page 70 and 71:
SUMMARY 51 . Dealing with incomplet
Page 72 and 73:
QUESTIONS FOR REVIEW 53 . Expert sy
Page 74 and 75:
Uncertainty management in rule-base
Page 76 and 77:
BASIC PROBABILITY THEORY 57 Table 3
Page 78 and 79:
BASIC PROBABILITY THEORY 59 single
Page 80 and 81:
BAYESIAN REASONING 61 Figure 3.1 Th
Page 82 and 83:
BAYESIAN REASONING 63 places an eno
Page 84 and 85:
FORECAST: BAYESIAN ACCUMULATION OF
Page 86 and 87:
Page 88 and 89:
Page 90 and 91:
Page 92 and 93:
BIAS OF THE BAYESIAN METHOD 73 Doma
Page 94 and 95:
CERTAINTY FACTORS THEORY AND EVIDEN
Page 96 and 97:
Page 98 and 99:
Page 100 and 101:
FORECAST: AN APPLICATION OF CERTAIN
Page 102 and 103:
SUMMARY 83 team also could assume t
Page 104 and 105:
REFERENCES 85 . Both Bayesian reaso
Page 106 and 107:
Fuzzy expert systems 4 In which we
Page 108 and 109:
FUZZY SETS 89 Range of logical valu
Page 110 and 111:
FUZZY SETS 91 Figure 4.2 Crisp (a)
Page 112 and 113:
FUZZY SETS 93 Figure 4.3 Crisp (a)
Page 114 and 115:
LINGUISTIC VARIABLES AND HEDGES 95
Page 116 and 117:
OPERATIONS OF FUZZY SETS 97 Table 4
Page 118 and 119:
OPERATIONS OF FUZZY SETS 99 Similar
Page 120 and 121:
OPERATIONS OF FUZZY SETS 101 Figure
Page 122 and 123:
FUZZY RULES 103 Example: NOT (tall
Page 124 and 125:
FUZZY RULES 105 Figure 4.9 Monotoni
Page 126 and 127:
FUZZY INFERENCE 107 determined by f
Page 128 and 129:
FUZZY INFERENCE 109 However, the OR
Page 130 and 131:
FUZZY INFERENCE 111 Step 4: Defuzzi
Page 132 and 133:
FUZZY INFERENCE 113 Figure 4.15 The
Page 134 and 135:
BUILDING A FUZZY EXPERT SYSTEM 115
Page 136 and 137:
Page 138 and 139:
Page 140 and 141:
Page 142 and 143:
Page 144 and 145:
SUMMARY 125 4.8 Summary In this cha
Page 146 and 147:
BIBLIOGRAPHY 127 9 What is clipping
Page 148:
BIBLIOGRAPHY 129 Zadeh, L.A. (1975)
Page 151 and 152:
132 FRAME-BASED EXPERT SYSTEMS Figu
Page 153 and 154:
134 FRAME-BASED EXPERT SYSTEMS of a
Page 155 and 156:
136 FRAME-BASED EXPERT SYSTEMS - -
Page 157 and 158:
Page 159 and 160:
140 FRAME-BASED EXPERT SYSTEMS and
Page 161 and 162:
142 FRAME-BASED EXPERT SYSTEMS such
Page 163 and 164:
Page 165 and 166:
146 FRAME-BASED EXPERT SYSTEMS valu
Page 167 and 168:
Page 169 and 170:
150 FRAME-BASED EXPERT SYSTEMS In a
Page 171 and 172:
Page 173 and 174:
Page 175 and 176:
156 FRAME-BASED EXPERT SYSTEMS illu
Page 177 and 178:
158 FRAME-BASED EXPERT SYSTEMS Area
Page 179 and 180:
160 FRAME-BASED EXPERT SYSTEMS ) On
Page 181 and 182:
162 FRAME-BASED EXPERT SYSTEMS The
Page 183 and 184:
164 FRAME-BASED EXPERT SYSTEMS Bibl
Page 185 and 186:
166 ARTIFICIAL NEURAL NETWORKS What
Page 187 and 188:
168 ARTIFICIAL NEURAL NETWORKS Tabl
Page 189 and 190:
170 ARTIFICIAL NEURAL NETWORKS Figu
Page 191 and 192:
172 ARTIFICIAL NEURAL NETWORKS Step
Page 193 and 194:
174 ARTIFICIAL NEURAL NETWORKS In F
Page 195 and 196:
176 ARTIFICIAL NEURAL NETWORKS Why
Page 197 and 198:
178 ARTIFICIAL NEURAL NETWORKS To p
Page 199 and 200:
180 ARTIFICIAL NEURAL NETWORKS (b)
Page 201 and 202:
182 ARTIFICIAL NEURAL NETWORKS Then
Page 203 and 204:
184 ARTIFICIAL NEURAL NETWORKS The
Page 205 and 206:
Page 207 and 208:
Page 209 and 210:
Page 211 and 212:
192 ARTIFICIAL NEURAL NETWORKS In o
Page 213 and 214:
194 ARTIFICIAL NEURAL NETWORKS wher
Page 215 and 216:
Page 217 and 218:
198 ARTIFICIAL NEURAL NETWORKS In t
Page 219 and 220:
200 ARTIFICIAL NEURAL NETWORKS Ther
Page 221 and 222:
Page 223 and 224:
204 ARTIFICIAL NEURAL NETWORKS Here
Page 225 and 226:
206 ARTIFICIAL NEURAL NETWORKS The
Page 227 and 228:
Page 229 and 230:
Page 231 and 232:
Page 233 and 234:
214 ARTIFICIAL NEURAL NETWORKS phys
Page 235 and 236:
216 ARTIFICIAL NEURAL NETWORKS 10 D
Page 238 and 239:
Evolutionary computation 7 In which
Page 240 and 241:
SIMULATION OF NATURAL EVOLUTION 221
Page 242 and 243:
GENETIC ALGORITHMS 223 Figure 7.2 A
Page 244 and 245:
GENETIC ALGORITHMS 225 Figure 7.3 T
Page 246 and 247:
GENETIC ALGORITHMS 227 Figure 7.5 T
Page 248 and 249:
GENETIC ALGORITHMS 229 When necessa
Page 250 and 251:
GENETIC ALGORITHMS 231 A surface of
Page 252 and 253:
WHY GENETIC ALGORITHMS WORK 233 m x
Page 254 and 255:
MAINTENANCE SCHEDULING WITH GENETIC
Page 256 and 257:
Page 258 and 259:
Page 260 and 261:
Page 262 and 263:
EVOLUTION STRATEGIES 243 Define a s
Page 264 and 265:
GENETIC PROGRAMMING 245 Which metho
Page 266 and 267:
GENETIC PROGRAMMING 247 Table 7.3 T
Page 268 and 269:
GENETIC PROGRAMMING 249 Figure 7.15
Page 270 and 271:
Page 272 and 273:
Page 274 and 275:
QUESTIONS FOR REVIEW 255 . Evolutio
Page 276:
BIBLIOGRAPHY 257 Whitley, L.D. (199
Page 279 and 280:
260 HYBRID INTELLIGENT SYSTEMS repr
Page 281 and 282:
262 HYBRID INTELLIGENT SYSTEMS enti
Page 283 and 284:
264 HYBRID INTELLIGENT SYSTEMS Figu
Page 285 and 286:
266 HYBRID INTELLIGENT SYSTEMS Now
Page 287 and 288:
Page 289 and 290:
Page 291 and 292:
Page 293 and 294: 274 HYBRID INTELLIGENT SYSTEMS Figu
Page 297 and 298: 278 HYBRID INTELLIGENT SYSTEMS sets
Page 299 and 300: 280 HYBRID INTELLIGENT SYSTEMS How
Page 301 and 302: 282 HYBRID INTELLIGENT SYSTEMS In t
Page 311 and 312: 292 HYBRID INTELLIGENT SYSTEMS word
Page 313 and 314: 294 HYBRID INTELLIGENT SYSTEMS Step
Page 315 and 316: 296 HYBRID INTELLIGENT SYSTEMS Step
Page 317 and 318: 298 HYBRID INTELLIGENT SYSTEMS 4 De
Page 320 and 321: Knowledge engineering and data mini
Page 322 and 323: INTRODUCTION, OR WHAT IS KNOWLEDGE
Page 328 and 329: WILL AN EXPERT SYSTEM WORK FOR MY P
Page 336 and 337: WILL A FUZZY EXPERT SYSTEM WORK FOR
Page 342 and 343: WILL A NEURAL NETWORK WORK FOR MY P
Page 356 and 357: WILL GENETIC ALGORITHMS WORK FOR MY
Page 358 and 359: WILL A HYBRID INTELLIGENT SYSTEM WO
Page 368 and 369: DATA MINING AND KNOWLEDGE DISCOVERY
Page 380 and 381: SUMMARY 361 9.8 Summary In this cha
Page 382 and 383: REFERENCES 363 7 Why are fuzzy syst
Page 384 and 385: Glossary The glossary entries are c
Page 386 and 387: GLOSSARY 367 Back-propagation see B
Page 388 and 389: GLOSSARY 369 Clipping A common meth
Page 390 and 391: GLOSSARY 371 amounts of data in ord
Page 392 and 393: GLOSSARY 373 Evolution strategy A n
Page 394 and 395:
GLOSSARY 375 Fuzzy rule A condition
Page 396 and 397:
GLOSSARY 377 Heuristic search A sea
Page 398 and 399:
GLOSSARY 379 Knowledge base A basic
Page 400 and 401:
GLOSSARY 381 Method A procedure ass
Page 402 and 403:
GLOSSARY 383 Output layer The last
Page 404 and 405:
GLOSSARY 385 Roulette wheel selecti
Page 406 and 407:
GLOSSARY 387 the network differs fr
Page 408:
GLOSSARY 389 determines the strengt
Page 411 and 412:
392 AI TOOLS AND VENDORS EXSYS, Inc
Page 413 and 414:
394 AI TOOLS AND VENDORS M.4 A powe
Page 415 and 416:
396 AI TOOLS AND VENDORS CynapSys,
Page 417 and 418:
398 AI TOOLS AND VENDORS 215 Parkwa
Page 419 and 420:
400 AI TOOLS AND VENDORS text files
Page 421 and 422:
402 AI TOOLS AND VENDORS TransferTe
Page 423 and 424:
404 AI TOOLS AND VENDORS La Jolla,
Page 425 and 426:
406 AI TOOLS AND VENDORS Fax: +1 (3
Page 427 and 428:
408 INDEX belongs-to 137-8 Berkeley
Page 429 and 430:
410 INDEX fuzzification 107 fuzzifi
Page 431 and 432:
412 INDEX Mamdani, E. 20, 106 Mamda
Page 434:
INDEX 415 unsupervised learning 200
show all

AI - a Guide to Intelligent Systems.pdf - Member of EEPIS

Create successful ePaper yourself

Delete template?

Save as template?