Intelligent Systems for Engineers and Scientists, Second Edition (Electronic Engineering Systems)

This updated version of the best-selling Knowledge-Based Systems for Engineers and Scientists (CRC Press, 1993) embraces both the explicit knowledge-based models retained from the first edition and the implicit numerical models represented by neural networks and optimization algorithms. The title change to Intelligent Systems for Engineers and Scientists reflects its broader scope, incorporating knowledge-based systems, computational intelligence, and their hybrids. Clear and concise, the book shows the issues encountered in the development of applied systems and describes a wide range of intelligent systems techniques. The author describes each technique at the level of detail required to develop intelligent systems for real applications. Whether you are building intelligent systems or you simply want to know more about them, Intelligent Systems for Engineers and Scientists provides you with a detailed, up-to-date, and practical guide to solving real problems in science and engineering. This indispensable book provides everything in one volume: BREADTH - from knowledge-based systems to computational intelligence DEPTH - from introductory concepts to advanced specialist techniques SCOPE - from principles to practicalities Booknews A textbook Hopgood (Open U.) hopes will appeal to students in many scientific and engineering disciplines, not just in computer science, revised from , for which neither date or publisher is cited. Taking a practical approach to the issues encountered in developing applied systems, he describes a range of intelligent systems techniques using examples drawn from engineering and science specifically to illustrate the techniques rather than merely to survey current practice. Annotation c. Book News, Inc., Portland, OR (booknews.com) Intelligent Systems for Engineers and Scientists......Page 1 Preface......Page 4 The author......Page 6 Contents......Page 8 1.1 Intelligent systems......Page 16 Table of Contents......Page 0 1.2 Knowledge-based systems......Page 17 1.3 The knowledge base......Page 19 1.4 Deduction,abduction,and induction......Page 22 1.5 The inference engine......Page 23 1.6 Declarative and procedural programming......Page 24 1.7 Expert systems......Page 26 1.9 Search......Page 27 1.10 Computational intelligence......Page 30 1.11 Integration with other software......Page 31 Further reading......Page 32 2.1 Rules and facts......Page 33 2.2 A rule-based system for boiler control......Page 34 2.3 Rule examination and rule firing......Page 36 2.4 Maintaining consistency......Page 37 2.5 The closed-world assumption......Page 39 2.6 Use of variables within rules......Page 40 2.7 Forward-chaining (a data-driven strategy)......Page 42 2.7.1 Single and multiple instantiation of variables......Page 44 2.7.2 Rete algorithm......Page 46 2.8.2 Priority values......Page 48 2.8.3 Metarules......Page 49 2.9.1 The backward-chaining mechanism......Page 50 2.9.3 Variations of backward-chaining......Page 53 2.10 A hybrid strategy......Page 56 2.11 Explanation facilities......Page 59 2.12 Summary......Page 60 Further reading......Page 61 3.1 Sources of uncertainty......Page 62 3.2.1 Representing uncertainty by probability......Page 63 3.2.2 Direct application of Bayes’ theorem......Page 64 3.2.3 Likelihood ratios......Page 66 3.2.4 Using the likelihood ratios......Page 67 3.2.5 Dealing with uncertain evidence......Page 69 3.2.6 Combining evidence......Page 70 3.2.7 Combining Bayesian rules with production rules......Page 73 3.2.8 A worked example of Bayesian updating......Page 74 3.2.9 Discussion of the worked example......Page 77 3.2.10 Advantages and disadvantages of Bayesian updating......Page 78 3.3.1 Introduction......Page 79 3.3.2 Making uncertain hypotheses......Page 80 3.3.3 Logical combinations of evidence......Page 82 3.3.4 A worked example of certainty theory......Page 83 3.3.5 Discussion of the worked example......Page 85 3.4 Possibility theory:fuzzy sets and fuzzy logic......Page 86 3.4.1 Crisp sets and fuzzy sets......Page 87 3.4.2 Fuzzy rules......Page 90 3.4.3 Defuzzification......Page 92 Stage 1:scaling the membership functions......Page 93 Stage 2:finding the centroid......Page 94 Defuzzifying at the extremes......Page 95 A defuzzification anomaly......Page 96 3.5.1 Dempster–Shafer theory of evidence......Page 98 3.5.2 Inferno......Page 99 3.6 Summary......Page 100 References......Page 101 Further reading......Page 102 4.1 Objects and frames......Page 104 4.2 An illustrative example......Page 105 4.3 Introducing OOP......Page 107 4.4.2 Instances......Page 108 4.4.3 Attributes (or data members)......Page 109 4.4.5 Creation and deletion of instances......Page 110 4.5.1 Single inheritance......Page 113 4.5.2 Multiple and repeated inheritance......Page 115 4.5.3 Specialization of methods......Page 118 4.5.4 Browsers......Page 119 4.7 Unified Modeling Language (UML)......Page 120 4.8 Dynamic (or late)binding......Page 123 4.9 Message passing and function calls......Page 126 4.9.1 Pseudovariables......Page 127 4.9.2 Metaclasses......Page 128 4.10 Type checking......Page 129 4.11 Further aspects of OOP......Page 131 4.11.3 Overloading......Page 132 4.11.4 Active values and daemons......Page 134 4.12 Frame-based systems......Page 135 4.13 Summary......Page 137 Further reading......Page 139 5.1 Characteristics of an intelligent agent......Page 141 5.2 Agents and objects......Page 143 5.3.2 Emergent behavior architectures......Page 144 5.3.3 Knowledge-level architectures......Page 145 5.4 Multiagent systems......Page 146 5.4.1 Benefits of a multiagent system......Page 147 5.4.2 Building a multiagent system......Page 149 Contract nets......Page 150 CPS framework......Page 151 Shifting Matrix Management (SMM)......Page 152 5.4.3 Communication between agents......Page 154 5.5 Summary......Page 155 References......Page 156 Further reading......Page 157 6.1 Introduction......Page 158 6.2.1 Overview......Page 160 6.2.2 Learning viewed as a search problem......Page 162 Replacing constants with variables......Page 164 Using conjunctions and disjunctions......Page 165 Moving up or down a hierarchy......Page 166 Chunking......Page 167 Abstraction links and index links......Page 168 6.3.2 Retrieving cases......Page 170 Parameterization......Page 171 Reinstantiation......Page 172 6.3.4 Dealing with mistaken conclusions......Page 173 References......Page 174 Further reading......Page 175 7.2 The search space......Page 176 7.3 Searching the search space......Page 178 7.4.1 Hill-climbing......Page 179 7.4.4 Conjugate gradient descent or ascent......Page 180 7.5 Simulated annealing......Page 181 7.6 Genetic algorithms......Page 184 Chromosomes......Page 185 Algorithm outline......Page 186 Crossover......Page 187 Validity check......Page 188 Fitness-proportionate selection......Page 189 Linear fitness scaling......Page 191 Rank selection......Page 193 Truncation selection......Page 194 Elitism......Page 195 7.6.3 Gray code......Page 196 7.6.4 Variable length chromosomes......Page 197 Inversion......Page 199 7.6.6 Selecting GA parameters......Page 200 7.6.9 Finding multiple optima......Page 201 7.7 Summary......Page 202 References......Page 203 Further reading......Page 205 8.1 Introduction......Page 206 8.2.1 Nonlinear estimation......Page 207 8.2.3 Clustering......Page 208 8.3 Nodes and interconnections......Page 209 8.4.1 Network topology......Page 211 8.4.2 Perceptrons as classifiers......Page 213 8.4.3 Training a perceptron......Page 216 8.4.5 Some practical considerations......Page 219 8.5 The Hopfield network......Page 222 8.6 MAXNET......Page 223 8.7 The Hamming network......Page 224 8.8 Adaptive Resonance Theory (ART)networks......Page 225 8.9 Kohonen self-organizing networks......Page 227 8.10 Radial basis function networks......Page 228 8.11 Summary......Page 231 References......Page 232 Further reading......Page 233 Parameter setting......Page 234 9.2 Blackboard systems......Page 235 9.3 Genetic-fuzzy systems......Page 237 9.4 Neuro-fuzzy systems......Page 238 9.6 Clarifying and verifying neural networks......Page 240 9.7 Learning classifier systems......Page 241 References......Page 242 Further reading......Page 243 10.1 A range of intelligent systems tools......Page 244 10.4 Artificial intelligence languages......Page 245 10.4.1 Lists......Page 246 10.4.2 Other data types......Page 247 10.4.3 Programming environments......Page 248 10.5.1 Background......Page 249 10.5.2 Lisp functions......Page 250 10.5.3 A worked example......Page 254 10.6.2 A worked example......Page 261 10.6.3 Backtracking in Prolog......Page 268 10.7 Comparison of AI languages......Page 272 References......Page 273 Further reading......Page 274 PROSPECTOR......Page 275 11.2 Deduction and abduction for diagnosis......Page 276 Explicit modeling of uncertainty......Page 278 Hypothesize-and-test......Page 279 11.3.1 Shallow knowledge......Page 280 11.3.2 Deep knowledge......Page 281 11.3.3 Combining shallow and deep knowledge......Page 282 11.4.1 The limitations of rules......Page 283 11.4.2 Modeling function,structure,and state......Page 284 Function......Page 285 Structure......Page 287 State......Page 288 11.4.3 Using the model......Page 292 11.4.4 Monitoring......Page 293 The shotgun approach......Page 295 11.4.6 Fault simulation......Page 296 11.4.7 Fault repair......Page 297 11.4.9 Summary of model-based reasoning......Page 298 11.5 Case study:a blackboard system for interpreting ultrasonic images......Page 299 11.5.1 Ultrasonic imaging......Page 300 11.5.2 Knowledge sources in ARBS......Page 302 11.5.3 Rules in ARBS......Page 304 11.5.4 Inference engines in ARBS......Page 308 Gathering the evidence......Page 311 11.5.6 The use of neural networks......Page 313 Combining the two applications of neural networks......Page 315 11.5.7 Rules for verifying neural networks......Page 316 11.6 Summary......Page 317 References......Page 318 Further reading......Page 321 12.1 The design process......Page 322 12.2 Design as a search problem......Page 325 12.3 Computer aided design......Page 327 12.4.2 Alternative views of a network......Page 328 12.4.3 Implementation......Page 329 Information stream......Page 331 12.5 Conceptual design......Page 332 12.6 Constraint propagation and truth maintenance......Page 336 12.7.1 Conceptual design......Page 339 12.7.2 Optimization and evaluation......Page 343 12.8.1 Overview......Page 347 12.8.2 Merit indices......Page 348 12.8.4 Two-stage selection......Page 350 12.8.5 Constraint relaxation......Page 351 12.8.6 A naive approach to scoring......Page 355 12.8.7 A better approach to scoring......Page 357 12.8.8 Case study:the design of a kettle......Page 359 12.8.9 Reducing the search space by classification......Page 360 Secondary damage......Page 363 12.10 Summary......Page 365 References......Page 366 Further reading......Page 368 13.1 Introduction......Page 369 13.2 Classical planning systems......Page 370 13.3.1 General description......Page 372 13.3.2 An example problem......Page 373 13.3.3 A simple planning system in Prolog......Page 377 13.4.1 Maintaining a world model......Page 381 13.4.2 Deductive rules......Page 382 13.5.1 Description......Page 383 13.5.2 Benefits of hierarchical planning......Page 385 13.5.3 Hierarchical planning with ABSTRIPS......Page 387 13.6.1 Partial ordering of plans......Page 392 13.7.1 The problem......Page 394 13.7.2 Some approaches to scheduling......Page 396 13.8.1 Constraints and preferences......Page 397 Capacity constraints......Page 398 13.8.3 Identifying the critical sets of operations......Page 400 13.8.4 Sequencing in the disjunctive case......Page 401 13.8.5 Sequencing in the nondisjunctive case......Page 402 13.8.6 Updating earliest start times and latest finish times......Page 403 13.8.7 Applying preferences......Page 407 13.8.8 Using constraints and preferences......Page 409 13.9 Replanning and reactive planning......Page 410 13.10 Summary......Page 411 Constraints......Page 412 References......Page 413 Further reading......Page 414 14.1 Introduction......Page 416 14.2.1 Open-loop control......Page 417 14.2.2 Feedforward control......Page 418 14.2.4 First-and second-order models......Page 419 14.2.5 Algorithmic control:the PID controller......Page 420 14.2.6 Bang-bang control......Page 422 14.3 Requirements of high-level (supervisory)control......Page 424 14.4 Blackboard maintenance......Page 425 14.5.1 Prioritization of processes and knowledge sources......Page 427 14.5.2 Approximation......Page 428 Approximate search......Page 429 Knowledge approximations......Page 430 14.5.3 Single and multiple instantiation......Page 431 14.6.1 Crisp and fuzzy control......Page 433 14.6.2 Firing fuzzy control rules......Page 434 14.6.3 Defuzzification......Page 435 14.6.4 Some practical examples of fuzzy controllers......Page 437 14.7.1 The conventional BOXES algorithm......Page 438 14.7.2 Fuzzy BOXES......Page 444 14.8.1 Direct association of state variables with action variables......Page 446 14.8.2 Estimation of critical state variables......Page 448 14.9.2 Collecting the data......Page 451 14.9.3 Using the data......Page 453 14.10 Summary......Page 454 References......Page 455 Further reading......Page 456 Speed......Page 458 15.2 Implementation......Page 459 15.3 Trends......Page 460 References......Page 461 This updated version of the best-selling Knowledge-Based Systems for Engineers and Scientists (CRC Press, 1993) embraces both the explicit knowledge-based models retained from the first edition and the implicit numerical models represented by neural networks and optimization algorithms. The title change to Intelligent Systems for Engineers and Scientists reflects its broader scope, incorporating knowledge-based systems, computational intelligence, and their hybrids. Clear and concise, the book shows the issues encountered in the development of applied systems and describes a wide range of intelligent systems techniques. The author describes each technique at the level of detail required to develop intelligent systems for real applications. Whether you are building intelligent systems or you simply want to know more about them, Intelligent Systems for Engineers and Scientists provides you with a detailed, up-to-date, and practical guide to solving real problems in science and engineering. This indispensable book provides everything in one volume: BREADTH - from knowledge-based systems to computational intelligence DEPTH - from introductory concepts to advanced specialist techniques SCOPE - from principles to practicalities "This updated version of the best-selling Knowledge-Based Systems for Engineers and Scientists embraces both the explicit knowledge-based models retained from the first edition and the implicit numerical models represented by neural networks and optimization algorithms. The title change to Intelligent Systems for Engineers and Scientists reflects its broader scope, incorporating knowledge-based systems, computational intelligence, and their hybrids."--Résumé de l'éditeur This second edition embraces both explicit knowledge-based models and the implicit numerical models represented by neural networks and organization algorithms. This book highlights the issues encountered in developing applied systems and describes a wide range of intelligent systems techniques