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WILL A HYBRID INTELLIGENT SYSTEM WORK FOR MY PROBLEM? 339 Figure 9.31 Performance graph and the best salesman’s routes created in a population of 20 chromosomes after 100 generations route. The fitness of each individual chromosome is determined as the reciprocal of the route length. In other words, the shorter the route, the fitter the chromosome. Once the fitness function is defined and genetic operators are constructed, we can implement and run the GA. As an example, let us consider the TSP for 20 cities placed in a 1 1square.First, we choose the size of a chromosome population and the number of generations to run. We might start with a relatively small population and after a few generations examine the solutions obtained. Figure 9.31 shows the best route created by 20 chromosomes after 100 generations. As we can see, this route is not optimal and obviously can be improved. Let us increase the size of the chromosome population and run the GA again. Figure 9.32(a) demonstrates the results. The total length of the route decreases by 20 per cent – a very significant improvement. But how do we know that the GA has actually found the optimal route? The fact is, we don’t. Only further trial runs on different sizes of chromosome populations with different rates of crossover and mutation can provide the answer. Let us, for example, increase the mutation rate up to 0.01. Figure 9.32(b) shows the results. Although the total distance decreased slightly, the salesman’s route is still similar to the one shown in Figure 9.32(a). Perhaps we might now attempt to increase the size of the chromosome population and rerun the GA. However, it is highly unlikely that we would achieve a noticeably better solution. Can we be sure that this route is the optimal one? Of course, we wouldn’t bet on it! However, after several runs, we can be absolutely sure that the route we obtained is a good one. 9.6 Will a hybrid intelligent system work for my problem? Solving complex real-world problems requires an application of complex intelligent systems that combine the advantages of expert systems, fuzzy logic, neural networks and evolutionary computation. Such systems can integrate
340 KNOWLEDGE ENGINEERING AND DATA MINING Figure 9.32 Performance graphs and the best routes created in a population of 200 chromosomes: (a) mutation rate is 0.001; (b) mutation rate is 0.01 human-like expertise in a specific domain with abilities to learn and adapt to a rapidly changing environment. Although the field of hybrid intelligent systems is still evolving, and most hybrid tools are not yet particularly effective, neuro-fuzzy systems have already matured as an advanced technology with numerous successful applications. While neural networks can learn from data, the key benefit of fuzzy logic lies in its ability to model decision-making of humans. Case study 8: Neuro-fuzzy decision-support systems I want to develop an intelligent system for diagnosing myocardial perfusion from cardiac images. I have a set of cardiac images as well as the clinical notes and physician’s interpretation. Will a hybrid system work for this problem? Diagnosis in modern cardiac medicine is based on the analysis of SPECT (Single Proton Emission Computed Tomography) images. By injecting a patient with
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MICHAEL NEGNEVITSKY Artificial Inte
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We work with leading authors to dev
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Pearson Education Limited Edinburgh
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Contents Preface Preface to the sec
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CONTENTS ix 7 Evolutionary computat
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Preface ‘The only way not to succ
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PREFACE xiii In Chapter 3, we prese
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Prefacetothesecondedition The main
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Introduction to knowledgebased inte
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INTELLIGENT MACHINES 3 Figure 1.1 T
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THE HISTORY OF ARTIFICIAL INTELLIGE
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SUMMARY 17 Although fuzzy systems a
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SUMMARY 19 Table 1.1 Period A summa
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QUESTIONS FOR REVIEW 21 or fuzzy se
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REFERENCES 23 Kosko, B. (1997). Fuz
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Rule-based expert systems 2 In whic
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RULES AS A KNOWLEDGE REPRESENTATION
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THE MAIN PLAYERS IN THE EXPERT SYST
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STRUCTURE OF A RULE-BASED EXPERT SY
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FUNDAMENTAL CHARACTERISTICS OF AN E
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FORWARD AND BACKWARD CHAINING INFER
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MEDIA ADVISOR: A DEMONSTRATION RULE
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MEDIA ADVISOR: A DEMONSTRATION RULE
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MEDIA: A DEMONSTRATION RULE-BASED E
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CONFLICT RESOLUTION 47 Does MEDIA A
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CONFLICT RESOLUTION 49 . Fire the m
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SUMMARY 51 . Dealing with incomplet
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QUESTIONS FOR REVIEW 53 . Expert sy
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Uncertainty management in rule-base
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BASIC PROBABILITY THEORY 57 Table 3
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BASIC PROBABILITY THEORY 59 single
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BAYESIAN REASONING 61 Figure 3.1 Th
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BAYESIAN REASONING 63 places an eno
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FORECAST: BAYESIAN ACCUMULATION OF
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BIAS OF THE BAYESIAN METHOD 73 Doma
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CERTAINTY FACTORS THEORY AND EVIDEN
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FORECAST: AN APPLICATION OF CERTAIN
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SUMMARY 83 team also could assume t
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REFERENCES 85 . Both Bayesian reaso
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Fuzzy expert systems 4 In which we
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FUZZY SETS 89 Range of logical valu
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FUZZY SETS 91 Figure 4.2 Crisp (a)
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FUZZY SETS 93 Figure 4.3 Crisp (a)
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LINGUISTIC VARIABLES AND HEDGES 95
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OPERATIONS OF FUZZY SETS 97 Table 4
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OPERATIONS OF FUZZY SETS 99 Similar
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OPERATIONS OF FUZZY SETS 101 Figure
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FUZZY RULES 103 Example: NOT (tall
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FUZZY RULES 105 Figure 4.9 Monotoni
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FUZZY INFERENCE 107 determined by f
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FUZZY INFERENCE 109 However, the OR
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FUZZY INFERENCE 111 Step 4: Defuzzi
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FUZZY INFERENCE 113 Figure 4.15 The
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BUILDING A FUZZY EXPERT SYSTEM 115
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SUMMARY 125 4.8 Summary In this cha
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BIBLIOGRAPHY 127 9 What is clipping
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BIBLIOGRAPHY 129 Zadeh, L.A. (1975)
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132 FRAME-BASED EXPERT SYSTEMS Figu
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134 FRAME-BASED EXPERT SYSTEMS of a
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136 FRAME-BASED EXPERT SYSTEMS - -
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140 FRAME-BASED EXPERT SYSTEMS and
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142 FRAME-BASED EXPERT SYSTEMS such
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146 FRAME-BASED EXPERT SYSTEMS valu
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150 FRAME-BASED EXPERT SYSTEMS In a
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156 FRAME-BASED EXPERT SYSTEMS illu
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158 FRAME-BASED EXPERT SYSTEMS Area
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160 FRAME-BASED EXPERT SYSTEMS ) On
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162 FRAME-BASED EXPERT SYSTEMS The
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164 FRAME-BASED EXPERT SYSTEMS Bibl
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166 ARTIFICIAL NEURAL NETWORKS What
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168 ARTIFICIAL NEURAL NETWORKS Tabl
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170 ARTIFICIAL NEURAL NETWORKS Figu
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172 ARTIFICIAL NEURAL NETWORKS Step
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174 ARTIFICIAL NEURAL NETWORKS In F
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176 ARTIFICIAL NEURAL NETWORKS Why
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178 ARTIFICIAL NEURAL NETWORKS To p
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180 ARTIFICIAL NEURAL NETWORKS (b)
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182 ARTIFICIAL NEURAL NETWORKS Then
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184 ARTIFICIAL NEURAL NETWORKS The
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192 ARTIFICIAL NEURAL NETWORKS In o
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194 ARTIFICIAL NEURAL NETWORKS wher
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198 ARTIFICIAL NEURAL NETWORKS In t
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200 ARTIFICIAL NEURAL NETWORKS Ther
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204 ARTIFICIAL NEURAL NETWORKS Here
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206 ARTIFICIAL NEURAL NETWORKS The
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214 ARTIFICIAL NEURAL NETWORKS phys
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216 ARTIFICIAL NEURAL NETWORKS 10 D
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Evolutionary computation 7 In which
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SIMULATION OF NATURAL EVOLUTION 221
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GENETIC ALGORITHMS 223 Figure 7.2 A
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GENETIC ALGORITHMS 225 Figure 7.3 T
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GENETIC ALGORITHMS 227 Figure 7.5 T
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GENETIC ALGORITHMS 229 When necessa
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GENETIC ALGORITHMS 231 A surface of
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WHY GENETIC ALGORITHMS WORK 233 m x
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MAINTENANCE SCHEDULING WITH GENETIC
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EVOLUTION STRATEGIES 243 Define a s
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GENETIC PROGRAMMING 245 Which metho
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GENETIC PROGRAMMING 247 Table 7.3 T
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GENETIC PROGRAMMING 249 Figure 7.15
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QUESTIONS FOR REVIEW 255 . Evolutio
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BIBLIOGRAPHY 257 Whitley, L.D. (199
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260 HYBRID INTELLIGENT SYSTEMS repr
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262 HYBRID INTELLIGENT SYSTEMS enti
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264 HYBRID INTELLIGENT SYSTEMS Figu
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266 HYBRID INTELLIGENT SYSTEMS Now
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278 HYBRID INTELLIGENT SYSTEMS sets
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280 HYBRID INTELLIGENT SYSTEMS How
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282 HYBRID INTELLIGENT SYSTEMS In t
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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
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Page 360 and 361: 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
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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
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GLOSSARY 389 determines the strengt
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392 AI TOOLS AND VENDORS EXSYS, Inc
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394 AI TOOLS AND VENDORS M.4 A powe
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396 AI TOOLS AND VENDORS CynapSys,
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398 AI TOOLS AND VENDORS 215 Parkwa
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400 AI TOOLS AND VENDORS text files
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402 AI TOOLS AND VENDORS TransferTe
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404 AI TOOLS AND VENDORS La Jolla,
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406 AI TOOLS AND VENDORS Fax: +1 (3
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408 INDEX belongs-to 137-8 Berkeley
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410 INDEX fuzzification 107 fuzzifi
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412 INDEX Mamdani, E. 20, 106 Mamda
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INDEX 415 unsupervised learning 200
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