Gespeichert in:
| Beteiligte Personen: | , |
|---|---|
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
Berlin [u.a.]
Springer
2011
|
| Schriftenreihe: | Intelligent systems reference library
17 |
| Schlagwörter: | |
| Links: | http://deposit.dnb.de/cgi-bin/dokserv?id=3710616&prov=M&dok_var=1&dok_ext=htm http://digitool.hbz-nrw.de:1801/webclient/DeliveryManager?pid=4307201&custom_att_2=simple_viewer http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=024469867&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| Umfang: | XIV, 450 S. graph. Darst. |
| ISBN: | 9783642210037 |
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Datensatz im Suchindex
| _version_ | 1819303793906417664 |
|---|---|
| adam_text | IMAGE 1
CONTENTS
1 EVOLUTION OF MODERN COMPUTATIONAL INTELLIGENCE 1
1.1 INTRODUCTION 1
1.2 ROOTS OF ARTIFICIAL INTELLIGENCE 3
1.3 MODERN ARTIFICIAL INTELLIGENCE 7
1.4 METAMODERN AI 1 1
2 PROBLEM SOLVING BY SEARCH 13
2.1 INTRODUCTION 13
2.2 WHAT IS SEARCH? 13
2.3 TREE BASED SEARCH 16
2.3.1 TERMINOLOGY 16
2.4 GRAPH SEARCH 17
2.5 SEARCH METHODS CLASSIFICATION 19
2.6 UNINFORMED SEARCH METHODS 19
2.6.1 BREADTH FIRST SEARCH 20
2.6.2 DEPTH FIRST SEARCH 24
2.6.3 BACKTRACKING SEARCH 26
2.6.4 DEPTH BOUNDED (LIMITED) DEPTH FIRST SEARCH 27
2.6.5 ITERATIVE DEEPENING DEPTH FIRST SEARCH 29
2.6.6 BRANCH AND BOUND (OR UNIFORM COST SEARCH) 32
2.6.7 BIDIRECTIONAL SEARCH 34
2.7 PERFORMANCE EVALUATION OF THE UNINFORMED SEARCH STRATEGIES 36 2.7.1
REMARKS AND DISCUSSIONS 36
2.7.2 REPEATED STATES 38
SUMMARY 38
REFERENCES 40
VERIFICATION QUESTIONS 42
EXERCISES 43
3 INFORMED (HEURISTIC) SEARCH 53
3.1 INTRODUCTION 53
3.2 HEURISTICS 54
3.3 BEST FIRST SEARCH 56
3.4 GREEDY SEARCH 57
3.5 A* SEARCH 63
3.6 COMPARISONS AND REMARKS 70
BIBLIOGRAFISCHE INFORMATIONEN HTTP://D-NB.INFO/1010922297
DIGITALISIERT DURCH
IMAGE 2
VIII CONTENTS
3.7 A* VARIANTS 70
3.7.1 ITERATIVE DEEPENING A* (IDA*) 71
3.7.2 SIMPLIFIED MEMORY BOUNDED A* (SMA*) 71
3.7.3 RECURSIVE BEST-FIRST SEARCH (RBFS) 75
3.7.4 D* ALGORITHM 75
3.7.5 BEAM SEARCH 76
SUMMARY 76
REFERENCES 77
VERIFICATION QUESTIONS 79
EXERCISES 79
4 ITERATIVE SEARCH 83
4.1 INTRODUCTION 83
4.2 HILL CLIMBING 84
4.3 SIMULATED ANNEALING 92
4.4 TABU SEARCH 98
4.5 MEANS ENDS 103
4.6 SUMMARY 104
REFERENCES 105
VERIFICATION QUESTIONS 107
EXERCISES 108
5 ADVERSARIAL SEARCH I LL
5.1 INTRODUCTION I LL
5.2 MIN-MAX ALGORITHM 112
5.2.1 DESIGNING THE UTILITY FUNCTION 113
5.3 ALPHA-BETA PRUNING 119
5.4 COMPARISONS AND DISCUSSIONS 123
SUMMARY 123
REFERENCES 125
VERIFICATION QUESTIONS 125
EXERCISES 126
6 KNOWLEDGE REPRESENTATION AND REASONING 131
6.1 INTRODUCTION 131
6.2 PROPOSITIONAL LOGIC 132
6.2.1 LOGICAL OPERATORS 133
6.2.2 TERMINOLOGY 135
6.2.3 INFERENCE 137
6.2.3.1 INTRODUCTION 138
6.2.3.2 ELIMINATION 138
6.3 FIRST ORDER PREDICATE LOGIC (FOPL) 139
6.3.1 PREDICATE CALCULUS 139
6.3.2 FOPL ALPHABET 140
IMAGE 3
CONTENTS IX
6.4 RESOLUTION IN PROPOSITIONAL LOGIC AND FOPL 142
6.4.1 RESOLUTION IN PROPOSITIONAL LOGIC 143
6.4.2 RESOLUTION IN FOPL 144
SUMMARIES 145
REFERENCES 146
VERIFICATION QUESTIONS 146
EXERCISES 147
7 RULE-BASED EXPERT SYSTEMS 149
7.1 INTRODUCTION 149
7.2 ELEMENTS OF A RULE-BASED SYSTEM 150
7.2.1 RULES 151
7.2.1.1 RULES CLASSIFICATION 152
7.3 STRUCTURE OF A RULE-BASED EXPERT SYSTEM 154
7.4 TYPES OF RULE-BASED EXPERT SYSTEMS 156
7.4.1 FORWARD CHAINING SYSTEMS 158
7.4.2 BACKWARD CHAINING SYSTEMS 165
7.4.3 FORWARD CHAINING OR BACKWARD CHAINING? WHICH ONE SHOULD APPLY? 172
7.5 CONFLICT RESOLUTION 172
7.6 BENEFITS AND CAPABILITIES OF RULE BASED EXPERT SYSTEMS 175 7.7 TYPES
OF EXPERT SYSTEMS 176
7.8 EXAMPLES OF EXPERT SYSTEMS 177
SUMMARIES 179
REFERENCES 180
VERIFICATION QUESTIONS 181
EXERCISES 181
8 MANAGING UNCERTAINTY IN RULE BASED EXPERT SYSTEMS 187
8.1 WHAT IS UNCERTAINTY AND HOW TO DEAL WITH IT? 187
8.2 BAYESIAN THEORY 189
8.2.1 CLASSICAL PROBABILITY THEORY 189
8.2.2 BAYES RULES 191
8.2.3 BAYESIAN REASONING 193
8.2.4 BAYESIAN NETWORKS 196
8.2.4.1 INFERENCE IN BAYESIAN NETWORKS 198
8.2.4.2 VARIABLE ORDERING IN BAYESIAN NETWORKS 200 8.2.4.3 FACTS ABOUT
BAYESIAN NETWORKS 201
8.3 CERTAINTY FACTORS 202
8.3.1 CALCULATING CERTAINTY FACTORS 204
8.3.1.1 MEASURE OF BELIEF. 204
8.3.1.2 MEASURE OF DISBELIEF 204
8.3.2 COMBINING CERTAINTY FACTORS 205
8.3.2.1 MULTIPLE RULES PROVIDING EVIDENCE FOR THE SAME CONCLUSION 205
IMAGE 4
X CONTENTS
8.3.2.2 MULTIPLE RULES WITH UNCERTAIN EVIDENCE FOR THE SAME CONCLUSION
206
SUMMARIES 212
REFERENCES 213
VERIFICATION QUESTIONS 214
EXERCISES 214
9 FUZZY EXPERT SYSTEMS 219
9.1 INTRODUCTION 219
9.2 FUZZY SETS 220
9.2.1 REPRESENTING FUZZY SETS 223
9.2.2 OPERATIONS WITH FUZZY SETS 228
9.2.2.1 COMPLEMENT 228
9.2.2.2 CONTAINMENT 229
9.2.2.3 INTERSECTION 230
9.2.2.4 UNION 230
9.2.2.5 EQUALITY 231
9.2.2.6 ALGEBRAIC PRODUCT 231
9.2.2.6 ALGEBRAIC SUM 231
9.2.3 PROPRIETIES OF FUZZY SETS 231
9.2.3.1 ASSOCIATIVITY 232
9.2.3.2 DISTRIBUTIVITY 232
9.2.3.3 COMMUTATIVITY 232
9.2.3.4 TRANSITIVITY 233
9.2.3.5 IDEMPOTENCY 233
9.2.3.6 IDENTITY 233
9.2.3.7 INVOLUTION 234
9.2.3.7 DE MORGAN S LAWS 234
9.2.4 HEDGES 235
9.3 FUZZY RULES 238
9.4 FUZZY INFERENCE 239
9.4.1 FUZZYFICATION 240
9.4.2 RULE EVALUATION AND INFERENCE 243
9.4.3 DEFUZZYFICATION 246
9.4.4 MAMDANI FUZZY MODEL 247
9.4.5 SUGENO FUZZY MODEL 251
9.4.6 TSUKAMOTO FUZZY MODEL 254
SUMMARIES 256
REFERENCES 257
VERIFICATION QUESTIONS 258
EXERCISES 259
10 MACHINE LEARNING 261
10.1 INTRODUCTION 261
10.2 TERMINOLOGY 263
10.3 LEARNING STEPS 264
IMAGE 5
CONTENTS XI
10.4 LEARNING SYSTEMS CLASSIFICATION 265
10.4.1 CLASSIFICATION BASED ON GOAL, TASKS, TARGET FUNCTION 265 10.4.2
CLASSIFICATION BASED ON THE MODEL 266
10.4.3 CLASSIFICATION BASED ON THE LEARNING RULES 266 10.4.4
CLASSIFICATION BASED ON EXPERIENCE 266
10.5 MACHINE LEARNING EXAMPLE 267
REFERENCES 268
11 DECISION TREES 269
11.1 INTRODUCTION 269
11.2 BUILDING A DECISION TREE 271
11.2.1 TOP-DOWN INDUCTION OF DECISION TREE 271
11.2.2 HOW TO CHOSE THE BEST ATTRIBUTE? 273
11.3 OVERFITTING IN DECISION TREES 276
11.3.1 PRUNING A DECISION TREE 278
11.4 DECISION TREES VARIANTS 278
SUMMARIES 279
REFERENCES 280
VERIFICATION QUESTIONS 280
12 ARTIFICIAL NEURAL NETWORKS 281
12.1 INTRODUCTION 281
12.2 SIMILARITIES BETWEEN BIOLOGICAL AND ARTIFICIAL NEURAL NETWORKS 282
12.3 NEURAL NETWORKS TYPES 284
12.3.1 LAYERED FEED-FORWARD NETWORK 284
12.3.2 THE PERCEPTRON 285
12.3.3 FEEDFORWARD RADIAL BASIS FUNCTION (RBF) NETWORK 285 12.3.4
RECURRENT NETWORKS 285
12.3.4.1 HOPFIELD NEURAL NETWORK 285
12.3.4.2 SIMPLE RECURRENT NETWORK (SRN) ELMAN STYLE 286 12.3.4.3 SIMPLE
RECURRENT NETWORK (SRN) JORDAN STYLE 286 12.3.5 SELF-ORGANIZING MAPS 286
12.4 THE PERCEPTRON ...286
12.4.1 ACTIVATION FUNCTIONS 287
12.4.2 HOW THE PERCEPTRON LEARNS A TASK? 290
12.4.2.1 THE PERCEPTRON RULE 292
12.4.2.2 DELTA RULE 293
12.4.3 EXAMPLE: PERCEPTRON FOR OR FUNCTION 294
12.4.4 LIMITATIONS OF THE PERCEPTRON 299
12.5 MULTI-LAYER PERCEPTRON 299
12.5.1 BACKPROPAGATION LEARNING ALGORITHM 303
12.5.1.1 BACKPROPAGATION LEARNING: NETWORK WITH ONE HIDDEN LAYER 303
12.5.1.2 BACKPROPAGATION LEARNING: NETWORK WITH TWO HIDDEN LAYERS 310
IMAGE 6
XII CONTENTS
12.5.2 RELATIONSHIP BETWEEN DATASET, NUMBER OF WEIGHTS AND
CLASSIFICATION ACCURACY 316
12.5.3 IMPROVING EFFICIENCY OF BACKPROPAGATION LEARNING 317 SUMMARIES
318
REFERENCES 319
VERIFICATION QUESTIONS 321
EXERCISES 321
13 ADVANCED ARTIFICIAL NEURAL NETWORKS 325
13.1 INTRODUCTION 325
13.2 JORDAN NETWORK 325
13.3 ELMAN NETWORK 327
13.4 HOPFIELD NETWORK 328
13.5 SELF ORGANIZING NETWORKS 329
13.5.1 HEBB NETWORKS 329
13.5.2 SELF ORGANIZING MAPS 332
13.5.2.1 KOHONEN SELF ORGANIZING MAPS: THE ALGORITHM... 334 13.6
NEOCOGNITRON 335
13.7 APPLICATION OF NEURAL NETWORKS 340
SUMMARIES 342
REFERENCES 343
VERIFICATION QUESTIONS 344
14 EVOLUTIONARY ALGORITHMS 345
14.1 INTRODUCTION 345
14.2 HOW TO BUILD AN EVOLUTIONARY ALGORITHM? 347
14.2.1 DESIGNING A REPRESENTATION 348
14.2.2 INITIALIZING THE POPULATION 348
14.2.3 EVALUATING AN INDIVIDUAL 349
14.2.4 SELECTION MECHANISM 350
14.2.5 DESIGNING SUITABLE VARIATION OPERATORS 350
14.2.5.1 MUTATION OPERATOR 350
14.2.5.2 CROSSOVER (RECOMBINATION) OPERATOR 350 14.2.6 DESIGNING A
REPLACEMENT SCHEME 351
14.2.7 DESIGNING A WAY TO STOP THE ALGORITHM 351
14.3 GENETIC ALGORITHMS 351
14.3.1 REPRESENTING THE INDIVIDUALS 352
14.3.1.1 BINARY REPRESENTATION 352
14.3.1.2 REAL REPRESENTATION 353
14.3.1.3 INTEGER REPRESENTATION 354
14.3.1.4 ORDER-BASED REPRESENTATION 354
14.3.2 INITIALIZING THE POPULATION 355
14.3.3 SELECTION MECHANISMS 356
14.3.3.1 TOURNAMENT SELECTION 356
14.3.3.2 FITNESS PROPORTIONAL SELECTION 357
14.3.3.3 ROULETTE WHEEL SELECTION 357
IMAGE 7
CONTENTS XIII
14.3.3.4 STOCHASTIC UNIVERSAL SAMPLING 359
14.3.3.5 RANK BASED SELECTION 360
14.3.3.6 LOCAL SELECTION 361
14.3.4 VARIATION OPERATORS 363
14.3.4.1 CROSSOVER OR RECOMBINATION 363
14.3.4.2 MUTATION 374
14.3.5 POPULATION MODELS 379
14.3.6 SURVIVOR SELECTION AND REINSERTION 380
14.3.6.1 LOCAL REINSERTION 380
14.3.6.2 GLOBAL REINSERTION 380
14.3.7 THE BASIC GENETIC ALGORITHM 381
SUMMARIES 382
REFERENCES 382
VERIFICATION QUESTIONS 384
EXERCISES 385
15 EVOLUTIONARY METAHEURISTICS 387
15.1 INTRODUCTION 387
15.2 REPRESENTATION 388
15.3 MUTATION 388
15.3.1 UNCORRELATED MUTATION WITH ONE A 389
15.3.2 UNCORRELATED MUTATION WITH N O S 389
15.3.3 CORRELATED MUTATION 390
15.4 RECOMBINATION 390
15.5 CONTROLLING THE EVOLUTION: SURVIVAL SELECTION 391
15.5.1 P, C STRATEGY 391
15.5.2 P + C STRATEGY 391
15.5.3 P/R, C STRATEGY 391
15.5.4 P/R + C STRATEGY 392
15.6 EVOLUTIONARY PROGRAMMING 392
15.6.1 REPRESENTATION 392
15.6.2 MUTATION 392
15.6.3 SURVIVAL SELECTION 393
15.7 GENETIC PROGRAMMING 393
15.7.1 REPRESENTATION 394
15.7.2 VARIATION OPERATORS 397
15.7.2.1 MUTATION 397
15.7.2.2 RECOMBINATION 397
15.7.2.3 BRANCH DUPLICATION 397
15.7.3 FITNESS FUNCTION 397
15.7.4 PARENT SELECTION 398
15.7.5 SURVIVAL SELECTION 398
15.7.6 GP VARIANTS 399
15.7.6.1 LINEAR GENETIC PROGRAMMING 399
15.7.6.2 MULTI-EXPRESSION PROGRAMMING 400
IMAGE 8
XIV CONTENTS
15.7.6.3 GENE EXPRESSION PROGRAMMING 402
15.7.6.4 GRAMMATICAL EVOLUTION 402
15.7.7 GP APPLICATIONS 405
SUMMARIES 405
REFERENCES 406
VERIFICATION QUESTIONS 406
16 SWARM INTELLIGENCE 409
16.1 INTRODUCTION 409
16.2 PARTICLE SWARM OPTIMIZATION 411
16.2.1 PARAMETERS OF PSO 413
16.3 ANT COLONIES OPTIMIZATION 415
16.3.1 ANT SYSTEM 416
SUMMARIES 418
REFERENCES 421
VERIFICATION QUESTIONS 422
EXERCISES 422
17 HYBRID INTELLIGENT SYSTEMS 423
17.1 INTRODUCTION 423
17.2 MODELS OF HYBRID COMPUTATIONAL INTELLIGENCE ARCHITECTURES 425
17.2.1 STAND-ALONE SYSTEMS 425
17.2.2 TRANSFORMATIONAL HYBRID INTELLIGENT SYSTEM 425 17.2.3
HIERARCHICAL HYBRID INTELLIGENT SYSTEM 426
17.2.4 INTEGRATED INTELLIGENT SYSTEM 427
17.3 NEURO-FUZZY SYSTEMS 427
17.3.1 COOPERATIVE AND CONCURRENT NEURO-FUZZY SYSTEMS 427 17.3.2 FUSED
NEURO FUZZY SYSTEMS 428
17.3.3 DISCUSSIONS 436
17.4 EVOLUTIONARY FUZZY SYSTEMS 436
17.4.1 EVOLUTIONARY - NEURO - FUZZY (EVONF) SYSTEMS 438 17.5
EVOLUTIONARY NEURAL NETWORKS (EANN) 439
17.5.1 GENERAL FRAMEWORK FOR EVOLUTIONARY NEURAL NETWORKS 440 17.5.2
EVOLUTIONARY SEARCH OF CONNECTION WEIGHTS 441 17.5.3 EVOLUTIONARY SEARCH
OF ARCHITECTURES 442
17.5.4 EVOLUTIONARY SEARCH OF LEARNING RULES 443
17.5.5 META LEARNING EVOLUTIONARY ARTIFICIAL NEURAL NETWORKS 444 17.6
HYBRID EVOLUTIONARY ALGORITHMS 446
SUMMARIES 448
REFERENCES 448
VERIFICATION QUESTIONS 450
EXERCISES 450
|
| any_adam_object | 1 |
| author | Grosan, Crina Abraham, Ajith 1968- |
| author_GND | (DE-588)123881315 |
| author_facet | Grosan, Crina Abraham, Ajith 1968- |
| author_role | aut aut |
| author_sort | Grosan, Crina |
| author_variant | c g cg a a aa |
| building | Verbundindex |
| bvnumber | BV039619423 |
| classification_rvk | ST 300 |
| ctrlnum | (OCoLC)725083348 (DE-599)DNB1010922297 |
| dewey-full | 006.3 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.3 |
| dewey-search | 006.3 |
| dewey-sort | 16.3 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
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| id | DE-604.BV039619423 |
| illustrated | Illustrated |
| indexdate | 2024-12-20T15:58:40Z |
| institution | BVB |
| isbn | 9783642210037 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-024469867 |
| oclc_num | 725083348 |
| open_access_boolean | |
| owner | DE-83 DE-384 |
| owner_facet | DE-83 DE-384 |
| physical | XIV, 450 S. graph. Darst. |
| publishDate | 2011 |
| publishDateSearch | 2011 |
| publishDateSort | 2011 |
| publisher | Springer |
| record_format | marc |
| series | Intelligent systems reference library |
| series2 | Intelligent systems reference library |
| spellingShingle | Grosan, Crina Abraham, Ajith 1968- Intelligent systems a modern approach Intelligent systems reference library Entscheidungsunterstützungssystem (DE-588)4191815-0 gnd Problemlösen (DE-588)4076358-4 gnd Soft Computing (DE-588)4455833-8 gnd Produktionsregelsystem (DE-588)4258171-0 gnd Suchverfahren (DE-588)4132315-4 gnd Expertensystem (DE-588)4113491-6 gnd Verteilte künstliche Intelligenz (DE-588)4281805-9 gnd Lernendes System (DE-588)4120666-6 gnd |
| subject_GND | (DE-588)4191815-0 (DE-588)4076358-4 (DE-588)4455833-8 (DE-588)4258171-0 (DE-588)4132315-4 (DE-588)4113491-6 (DE-588)4281805-9 (DE-588)4120666-6 |
| title | Intelligent systems a modern approach |
| title_auth | Intelligent systems a modern approach |
| title_exact_search | Intelligent systems a modern approach |
| title_full | Intelligent systems a modern approach Crina Grosan and Ajith Abraham |
| title_fullStr | Intelligent systems a modern approach Crina Grosan and Ajith Abraham |
| title_full_unstemmed | Intelligent systems a modern approach Crina Grosan and Ajith Abraham |
| title_short | Intelligent systems |
| title_sort | intelligent systems a modern approach |
| title_sub | a modern approach |
| topic | Entscheidungsunterstützungssystem (DE-588)4191815-0 gnd Problemlösen (DE-588)4076358-4 gnd Soft Computing (DE-588)4455833-8 gnd Produktionsregelsystem (DE-588)4258171-0 gnd Suchverfahren (DE-588)4132315-4 gnd Expertensystem (DE-588)4113491-6 gnd Verteilte künstliche Intelligenz (DE-588)4281805-9 gnd Lernendes System (DE-588)4120666-6 gnd |
| topic_facet | Entscheidungsunterstützungssystem Problemlösen Soft Computing Produktionsregelsystem Suchverfahren Expertensystem Verteilte künstliche Intelligenz Lernendes System |
| url | http://deposit.dnb.de/cgi-bin/dokserv?id=3710616&prov=M&dok_var=1&dok_ext=htm http://digitool.hbz-nrw.de:1801/webclient/DeliveryManager?pid=4307201&custom_att_2=simple_viewer http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=024469867&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV035704685 |
| work_keys_str_mv | AT grosancrina intelligentsystemsamodernapproach AT abrahamajith intelligentsystemsamodernapproach |