Springer handbook of computational intelligence:
Saved in:
| Other Authors: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Berlin [u.a.]
Springer
2015
|
| Subjects: | |
| Online Access: | Inhaltstext Inhaltsverzeichnis |
| Physical Description: | LVI, 1633 S. Ill., graph. Darst. 25 cm |
| ISBN: | 3662435047 9783662435045 |
Staff View
MARC
| LEADER | 00000nam a22000008c 4500 | ||
|---|---|---|---|
| 001 | BV042595136 | ||
| 003 | DE-604 | ||
| 005 | 20160217 | ||
| 007 | t | ||
| 008 | 150602s2015 gw ad|| |||| 00||| eng d | ||
| 015 | |a 14,N24 |2 dnb | ||
| 016 | 7 | |a 105180003X |2 DE-101 | |
| 020 | |a 3662435047 |9 3-662-43504-7 | ||
| 020 | |a 9783662435045 |c Gb. : EUR 373.43 (DE) (freier Pr.), EUR 383.90 (AT) (freier Pr.), sfr 465.00 (freier Pr.) |9 978-3-662-43504-5 | ||
| 024 | 3 | |a 9783662435045 | |
| 028 | 5 | 2 | |a Best.-Nr.: 80022299 |
| 035 | |a (OCoLC)881386996 | ||
| 035 | |a (DE-599)DNB105180003X | ||
| 040 | |a DE-604 |b ger |e rakddb | ||
| 041 | 0 | |a eng | |
| 044 | |a gw |c XA-DE-BE | ||
| 049 | |a DE-1050 |a DE-210 |a DE-12 | ||
| 082 | 0 | |a 006.3 |2 22/ger | |
| 084 | |a ST 300 |0 (DE-625)143650: |2 rvk | ||
| 084 | |a 620 |2 sdnb | ||
| 245 | 1 | 0 | |a Springer handbook of computational intelligence |c Janusz Kacprzyk ... (Eds.) |
| 246 | 1 | 3 | |a Handbook of computational intelligence |
| 264 | 1 | |a Berlin [u.a.] |b Springer |c 2015 | |
| 300 | |a LVI, 1633 S. |b Ill., graph. Darst. |c 25 cm | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 650 | 0 | 7 | |a Soft Computing |0 (DE-588)4455833-8 |2 gnd |9 rswk-swf |
| 655 | 7 | |0 (DE-588)4143413-4 |a Aufsatzsammlung |2 gnd-content | |
| 689 | 0 | 0 | |a Soft Computing |0 (DE-588)4455833-8 |D s |
| 689 | 0 | |5 DE-604 | |
| 700 | 1 | |a Kacprzyk, Janusz |d 1947- |0 (DE-588)110363248 |4 edt | |
| 776 | 0 | 8 | |i Erscheint auch als |n Online-Ausgabe |z 978-3-662-43505-2 |
| 856 | 4 | 2 | |m X:MVB |q text/html |u http://deposit.dnb.de/cgi-bin/dokserv?id=4682141&prov=M&dok_var=1&dok_ext=htm |3 Inhaltstext |
| 856 | 4 | 2 | |m DNB Datenaustausch |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028028304&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
| 943 | 1 | |a oai:aleph.bib-bvb.de:BVB01-028028304 | |
Record in the Search Index
| _version_ | 1809771578399916032 |
|---|---|
| adam_text |
CONTENTS
LIST OF ABBREVIATIONS
XLV
1 INTRODUCTION
JANUSZ KACPRZYK, WITOLD PEDRYCZ 1
1.1 DETAILS OF THE CONTENTS 2
1.2 CONCLUSIONS AND ACKNOWLEDGMENTS K
PART A FOUNDATIONS
2 MANY-VALUED AND FUZZY LOGICS
SIEGFRIED GOTTWALD 7
2.1 BASIC MANY-VALUED LOGICS 8
2.2 FUZZY SETS 11
2.3 T-NORM-BASED LOGICS 13
2.K PARTICULAR FUZZY LOGICS 16
2.5 SOME GENERALIZATIONS 21
2.6 EXTENSIONS WITH GRADED NOTIONS OF INFERENCE 23
2.7 SOME COMPLEXITY RESULTS 25
2.8 CONCLUDING REMARKS 27
REFERENCES 27
3
POSSIBILITY THEORY AND ITS APPLICATIONS: WHERE DO WE STAND?
DIDIER DUBOIS, HENRY PRADE 31
3.1 HISTORICAL BACKGROUND 32
3.2 BASIC NOTIONS OF POSSIBILITY THEORY 33
3.3 QUALITATIVE POSSIBILITY THEORY 38
3.^ QUANTITATIVE POSSIBILITY THEORY H5
3.5 SOME APPLICATIONS K9
3.6 SOME CURRENT RESEARCH LINES 53
REFERENCES 5^
4 AGGREGATION FUNCTIONS ON [0,1]
RADKO MESIAR, ANNA KOLESAROVA, MAGDA KOMORM'KOVA 61
K.L HISTORICAL AND INTRODUCTORY REMARKS 61
K.2 CLASSIFICATION OF AGGREGATION FUNCTIONS 63
PROPERTIES AND CONSTRUCTION METHODS 66
K.K CONCLUDING REMARKS 71
REFERENCES 72
5 MONOTONE MEASURES-BASED INTEGRALS
ERICH P. KLEMENT, RADKO MESIAR 75
5.1 PRELIMINARIES, CHOQUET, AND SUGENO INTEGRALS 76
5.2 BENVENUTI INTEGRAL 80
HTTP://D-NB.INFO/105180003X
5.3 UNIVERSAL INTEGRALS 82
5.A- GENERAL INTEGRALS WHICH ARE NOT UNIVERSAL 84
5.5 CONCLUDING REMARKS, APPLICATION FIELDS 86
REFERENCES 87
6 THE ORIGIN OF FUZZY EXTENSIONS
HUMBERTO BUSTINCE, EDURNE BARRENECHEA, JAVIER FERNANDEZ,
MIGUEL PAGOLA, JAVIER MONTERO 89
6.1 CONSIDERATIONS PRIOR TO THE CONCEPT OF EXTENSION OF FUZZY SETS 90
6.2 ORIGIN OF THE EXTENSIONS 93
6.3 TYPE-2 FUZZY SETS 94
6.4 INTERVAL-VALUED FUZZY SETS 98
6.5 ATANASSSOV'S INTUITIONISTIC FUZZY SETS OR BIPOLAR FUZZY SETS
OF TYPE 2 OR IF FUZZY SETS 103
6.6 ATANASSOV'S INTERVAL-VALUED INTUITIONISTIC FUZZY SETS 105
6.7 LINKS BETWEEN THE EXTENSIONS OF FUZZY SETS 106
6.8 OTHER TYPES OF SETS 106
6.9 CONCLUSIONS 108
REFERENCES 108
7
F-TRANSFORM
IRINA PERFILIEVA 113
7.1 FUZZY MODELING 113
7.2 FUZZY PARTITIONS 114
7.3 FUZZY TRANSFORM 117
7 .4 DISCRETE F-TRANSFORM 119
7.5 F-TRANSFORMS OF FUNCTIONS OF TWO VARIABLES 120
7.6 F
1
-TRANSFORM 121
7.7 APPLICATIONS 122
7.8 CONCLUSIONS 129
REFERENCES 129
8 FUZZY LINEAR PROGRAMMING AND DUALITY
JAROSLAV RAMIK, MILAN VLACH 131
8.1 PRELIMINARIES 132
8.2 FUZZY LINEAR PROGRAMMING 135
8.3 DUALITY IN FUZZY LINEAR PROGRAMMING 137
8.4 CONCLUSION 143
REFERENCES 143
9
BASIC SOLUTIONS OF FUZZY COALITIONAL GAMES
TOMAS KROUPA, MILAN VLACH 145
9.1 COALITIONAL GAMES WITH TRANSFERABLE UTILITY 146
9.2 COALITIONAL GAMES WITH FUZZY COALITIONS 150
9.3 FINAL REMARKS 155
REFERENCES 156
PART B FUZZY LOGIC
10 BASICS OF FUZZY SETS
JANOS C. FODOR, IMRE J. RUDAS 159
10.1 CLASSICAL MATHEMATICS AND LOGIC 160
10.2 FUZZY LOGIC, MEMBERSHIP FUNCTIONS, AND FUZZY SETS 160
10.3 CONNECTIVES IN FUZZY LOGIC 161
10.^ CONCLUDING REMARKS 168
REFERENCES 168
11 FUZZY RELATIONS: PAST, PRESENT, AND FUTURE
SUSANA MONTES, IGNACIO MONTES, TANIA IGLESIAS 171
11.1 FUZZY RELATIONS 172
11.2 CUT RELATIONS 174
11.3 FUZZY BINARY RELATIONS 174
11.4 PARTICULAR CASES OF FUZZY BINARY RELATIONS 179
11.5 PRESENT AND FUTURE OF FUZZY RELATIONS 180
REFERENCES
180
12 FUZZY IMPLICATIONS: PAST, PRESENT, AND FUTURE
MICHAT BACZYNSKI, BALASUBRAMANIAM JAYARAM, SEBASTIA MASSANET,
JOAN TORRENS 183
12.1 FUZZY IMPLICATIONS: EXAMPLES, PROPERTIES, AND CLASSES 184
12.2 CURRENT RESEARCH ON FUZZY IMPLICATIONS 187
12.3 FUZZY IMPLICATIONS IN APPLICATIONS 193
12.4 FUTURE OF FUZZY IMPLICATIONS 198
REFERENCES 199
13 FUZZY RULE-BASED SYSTEMS
LUIS MAGDALENA 203
13.1 COMPONENTS OF A FUZZY RULE BASED-SYSTEM 204
13.2 TYPES OF FUZZY RULE-BASED SYSTEMS 209
13.3 HIERARCHICAL FUZZY RULE-BASED SYSTEMS 213
13.4 FUZZY RULE-BASED SYSTEMS DESIGN 214
13.5 CONCLUSIONS 216
REFERENCES 217
14 INTERPRETABILITY OF FUZZY SYSTEMS:
CURRENT RESEARCH TRENDS AND PROSPECTS
JOSE M. ALONSO, CIRO CASTIELLO, CORRADO MENCAR 219
14.1 THE QUEST FOR INTERPRETABILITY 220
14.2 INTERPRETABILITY CONSTRAINTS AND CRITERIA 224
14.3 INTERPRETABILITY ASSESSMENT 227
14.4 DESIGNING INTERPRETABLE FUZZY SYSTEMS 229
14.5 INTERPRETABLE FUZZY SYSTEMS IN THE REAL WORLD 233
14.6 FUTURE RESEARCH TRENDS ON INTERPRETABLE FUZZY SYSTEMS 234
14.7 CONCLUSIONS 234
REFERENCES
235
15 FUZZY CLUSTERING - BASIC IDEAS AND OVERVIEW
SADAAKI MIYAMOTO 239
15.1 FUZZY CLUSTERING 239
15.2 FUZZY C-MEANS 239
15.3 HIERARCHICAL FUZZY CLUSTERING 245
15.4 CONCLUSION 246
REFERENCES 247
16 AN ALGEBRAIC MODEL OF REASONING TO SUPPORT ZADEH'S CWW
ENRICTRILLAS 249
16.1 A VIEW ON REASONING 249
16.2 MODELS 250
16.3 REASONING 251
16.4 REASONING AND LOGIC 254
16.5 A POSSIBLE SCHEME FOR AN ALGEBRAIC MODEL
OF COMMONSENSE REASONING 255
16.6 WEAK AND STRONG DEDUCTION: REFUTATIONS AND CONJECTURES
IN A BFA (WITH A FEW RESTRICTIONS) 260
16.7 TOWARD A CLASSIFICATION OF CONJECTURES 262
16.8 LAST REMARKS 264
16.9 CONCLUSIONS 265
REFERENCES 266
17 FUZZY CONTROL
CHRISTIAN MOEWES, RALFMIKUT, RUDOLF KRUSE 269
17.1 KNOWLEDGE-DRIVEN CONTROL 269
17.2 CLASSICAL CONTROL ENGINEERING 270
17.3 USING FUZZY RULES FOR CONTROL 271
17.4 A GLANCE AT SOME INDUSTRIAL APPLICATIONS 276
17.5 AUTOMATIC LEARNING OF FUZZY CONTROLLERS 279
17.6 CONCLUSIONS 281
REFERENCES 281
18 INTERVAL TYPE-2 FUZZY PID CONTROLLERS
TUFAN KUMBASAR, HANI HAGRAS 285
18.1 FUZZY CONTROL BACKGROUND 285
18.2 THE GENERAL FUZZY PID CONTROLLER STRUCTURE 286
18.3 SIMULATION STUDIES 291
18.4 CONCLUSION 292
REFERENCES 293
19
SOFT COMPUTING IN DATABASE AND INFORMATION MANAGEMENT
GUY DE TRE, SIAWOMIRZADROZNY 295
19.1 CHALLENGES FOR MODERN INFORMATION SYSTEMS 295
19.2 SOME PRELIMINARIES 296
19.3 SOFT COMPUTING IN INFORMATION MODELING 298
19.4 SOFT COMPUTING IN QUERYING 302
19.5 CONCLUSIONS 309
REFERENCES 309
20
APPLICATION OF FUZZY TECHNIQUES TO AUTONOMOUS ROBOTS
ISMAEL RODRIGUEZ FDEZ, MANUEL MUCIENTES, ALBERTO BUGARIN DIZ 313
20.1 ROBOTICS ARID FUZZY LOGIC 313
20.2 WALL-FOLLOWING 314
20.3 NAVIGATION 315
20.4 TRAJECTORY TRACKING 317
20.5 MOVING TARGET TRACKING 318
20.6 PERCEPTION 319
20.7 PLANNING 319
20.8 SLAM 320
20.9 COOPERATION 320
20.10 LEGGED ROBOTS 321
20.11 EXOSKELETONS AND REHABILITATION ROBOTS 322
20.12 EMOTIONAL ROBOTS 323
20.13 FUZZY MODELING 323
20.14 COMMENTS AND CONCLUSIONS 324
REFERENCES 325
PART C ROUGH SETS
21 FOUNDATIONS OF ROUGH SETS
ANDRZEJ SKOWRON, ANDRZEJ JANKOWSKI, ROMAN \N. SWINIARSKI 331
21.1 ROUGH SETS: COMMENTS ON DEVELOPMENT 331
21.2 VAGUE CONCEPTS 332
21.3 ROUGH SET PHILOSOPHY 333
21.^ INDISCERNIBILITY AND APPROXIMATION 333
21.5 DECISION SYSTEMS AND DECISION RULES 336
21.6 DEPENDENCIES 337
21.7 REDUCTION OF ATTRIBUTES 337
21.8 ROUGH MEMBERSHIP 338
21.9 DISCERNIBILITY AND BOOLEAN REASONING 339
21.10 ROUGH SETS AND INDUCTION 340
21.11 ROUGH SET-BASED GENERALIZATIONS 340
21.12 ROUGH SETS AND LOGIC 343
21.13 CONCLUSIONS 347
REFERENCES 347
22 ROUGH SET METHODOLOGY FOR DECISION AIDING
ROMAN STOWINSKI, SALVATORE GRECO, BENEDETTO MATARAZZO 349
22.1 DATA INCONSISTENCY AS A REASON FOR USING ROUGH SETS 350
22.2 THE NEED FOR REPLACING THE INDISCERNIBILITY RELATION
BY THE DOMINANCE RELATION WHEN REASONING ABOUT ORDINAL DATA . 351
22.3 THE DOMINANCE-BASED ROUGH SET APPROACH
TO MULTI-CRITERIA CLASSIFICATION 353
22.4 THE DOMINANCE-BASED ROUGH SET APPROACH TO MULTI-CRITERIA
CHOICE AND RANKING 361
22.5 IMPORTANT EXTENSIONS OF DRSA 366
22.6 DRSA TO OPERATIONAL RESEARCH PROBLEMS B66
22.7 CONCLUDING REMARKS ON DRSA
APPLIED TO MULTI-CRITERIA
DECISION PROBLEMS 367
REFERENCES B67
23
RULE INDUCTION FROM ROUGH APPROXIMATIONS
JERZY W. GRZYMALA-BUSSE 371
23.1 COMPLETE AND CONSISTENT DATA 371
23.2 INCONSISTENT DATA 375
23.3 DECISION TABLE WITH NUMERICAL ATTRIBUTES 377
23.4 INCOMPLETE DATA 378
23.5 CONCLUSIONS 384
REFERENCES 384
24
PROBABILISTIC ROUGH SETS
YIYU YAO, SALVATORE GRECO, ROMAN STOWIRISKI 387
24.1 MOTIVATION FOR STUDYING PROBABILISTIC ROUGH SETS 388
24.2 PAWLAK ROUGH SETS 388
24.3 A BASIC MODEL OF PROBABILISTIC ROUGH SETS 390
24.4 VARIANTS OF PROBABILISTIC ROUGH SETS 391
24.5 THREE FUNDAMENTAL ISSUES OF PROBABILISTIC ROUGH SETS 394
24.6 DOMINANCE-BASED ROUGH SET APPROACHES 398
24.7 A BASIC MODEL OF DOMINANCE-BASED PROBABILISTIC ROUGH SETS 399
24.8 VARIANTS OF PROBABILISTIC DOMINANCE-BASED ROUGH SET APPROACH .
400
24.9 THREE FUNDAMENTAL ISSUES OF PROBABILISTIC DOMINANCE-BASED
ROUGH SETS 403
24.10 CONCLUSIONS 409
REFERENCES 409
25
GENERALIZED ROUGH SETS
JINGTAO YAO, DAVIDE CIUCCI, YAN ZHANG 413
25.1 DEFINITION AND APPROXIMATIONS OF THE MODELS 414
25.2 THEORETICAL APPROACHES 420
25.3 CONCLUSION 422
REFERENCES 423
26
FUZZY-ROUGH HYBRIDIZATION
MASAHIRO INUIGUCHI, WEI-ZHI WU, CHRIS CORNELLS, NELE VERBIEST 425
26.1 INTRODUCTION TO FUZZY-ROUGH HYBRIDIZATION 425
26.2 CLASSIFICATION- VERSUS APPROXIMATION-ORIENTED
FUZZY ROUGH SET MODELS 427
26.3 GENERALIZED FUZZY BELIEF STRUCTURES WITH APPLICATION
IN FUZZY INFORMATION SYSTEMS 437
26.4 APPLICATIONS OF FUZZY ROUGH SETS 444
REFERENCES 447
PART D NEURAL NETWORKS
27
ARTIFICIAL NEURAL NETWORK MODELS
PETER TINO, LUBICA BENUSKOVA, ALESSANDRO SPERDUTI 455
27.1 BIOLOGICAL NEURONS 455
27.2 PERCEPTRON 456
27.3 MULTILAYERED FEED-FORWARD ANN MODELS 458
27.4 RECURRENT ANN MODELS 460
27.5 RADIAL BASIS FUNCTION ANN MODELS 464
27.6 SELF-ORGANIZING MAPS 465
27.7 RECURSIVE NEURAL NETWORKS 467
27.8 CONCLUSION 469
REFERENCES 470
28
DEEP AND MODULAR NEURAL NETWORKS
KE CHEN 473
28.1 OVERVIEW 473
28.2 DEEP NEURAL NETWORKS 474
28.3 MODULAR NEURAL NETWORKS 484
28.^ CONCLUDING REMARKS 492
REFERENCES 492
29
MACHINE LEARNING
JAMES T. KWOK, ZHI-HUA ZHOU, LEI XU 495
29.1 OVERVIEW 495
29.2 SUPERVISED LEARNING 497
29.3 UNSUPERVISED LEARNING 502
29.^ REINFORCEMENT LEARNING 510
29.5 SEMI-SUPERVISED LEARNING 513
29.6 ENSEMBLE METHODS 514
29.7 FEATURE SELECTION AND EXTRACTION 518
REFERENCES 519
30
THEORETICAL METHODS IN MACHINE LEARNING
BADONG CHEN, WEIFENG LIU, JOSE C. PRINCIPE 523
30.1 BACKGROUND OVERVIEW 524
30.2 REPRODUCING KERNEL HILBERT SPACES 525
30.3 ONLINE LEARNING WITH KERNEL ADAPTIVE FILTERS 527
30.4 ILLUSTRATION EXAMPLES 538
30.5 CONCLUSION 542
REFERENCES 542
31
PROBABILISTIC MODELING IN MACHINE LEARNING
DAVIDE BACCIU, PAULO J.G. LISBOA, ALESSANDRO SPERDUTI, THOMAS VILLMANN .
545
31.1 PROBABILISTIC AND INFORMATION-THEORETIC METHODS 545
31.2 GRAPHICAL MODELS 552
31.3 LATENT VARIABLE MODELS 560
31.4 MARKOV MODELS 565
31.5 CONCLUSION AND FURTHER READING 572
REFERENCES 573
32
KERNEL METHODS
MARCO SIGNORETTO, IOHAN A. K. SUYKENS 577
32.1 BACKGROUND 578
32.2 FOUNDATIONS OF STATISTICAL LEARNING 580
32.3 PRIMAL-DUAL METHODS 586
32.4 GAUSSIAN PROCESSES 593
32.5 MODEL SELECTION 596
32.6 MORE ON KERNELS 597
32.7 APPLICATIONS 600
REFERENCES 601
33
NEURODYNAMICS
ROBERT KOZMA, JUN WANG, ZHIGANG ZENG 607
33.1 DYNAMICS OF ATTRACTOR AND ANALOG NETWORKS 607
33.2 SYNCHRONY, OSCILLATIONS, AND CHAOS IN NEURAL NETWORKS 611
33.3 MEMRISTIVE NEURODYNAMICS 629
33.4 NEURODYNAMIC OPTIMIZATION 634
REFERENCES 639
34
COMPUTATIONAL NEUROSCIENCE - BIOPHYSICAL MODELING
OF NEURAL SYSTEMS
HARRISON STRATTON, JENNIE SI 649
34.1 ANATOMY AND PHYSIOLOGY OF THE NERVOUS SYSTEM 649
34.2 CELLS AND SIGNALING AMONG CELLS 652
34.3 MODELING BIOPHYSICALLY REALISTIC NEURONS 656
34.4 REDUCING COMPUTATIONAL COMPLEXITY
FOR LARGE NETWORK SIMULATIONS 660
34.5 CONCLUSIONS 662
REFERENCES 662
35
COMPUTATIONAL MODELS OF COGNITIVE AND MOTOR CONTROL
ALI A. MINAI 665
35.1 OVERVIEW 665
35.2 MOTOR CONTROL 667
35.3 COGNITIVE CONTROL AND WORKING MEMORY 670
35.4 CONCLUSION 674
REFERENCES 674
36
COGNITIVE ARCHITECTURES AND AGENTS
SEBASTIEN HELIE, RON SUN 683
36.1 BACKGROUND 683
36.2 ADAPTIVE CONTROL OF THOUGHT-RATIONAL (ACT-R) 685
36.3 SOAR 688
36.4 CLARION 690
36.5 COGNITIVE ARCHITECTURES AS MODELS OF MULTI-AGENT INTERACTION 693
36.6 GENERAL DISCUSSION 694
REFERENCES 695
37 EMBODIED INTELLIGENCE
ANGELA CANGELOSI, JOSH BONGARD, MARTIN H. FISCHER, STEFANO NOLFI 697
37.1 INTRODUCTION TO EMBODIED INTELLIGENCE 697
37.2 MORPHOLOGICAL COMPUTATION FOR BODY-BEHAVIOR COADAPTATION 698
37.3 SENSORY-MOTOR COORDINATION IN EVOLVING ROBOTS 701
37.4 DEVELOPMENTAL ROBOTICS FOR HIGHER ORDER EMBODIED COGNITIVE
CAPABILITIES 703
37.5 CONCLUSION 709
REFERENCES 711
38 NEUROMORPHIC ENGINEERING
GIACOMO INDIVERI 715
38.1 THE ORIGINS 715
38.2 NEURAL AND NEUROMORPHIC COMPUTING 716
38.3 THE IMPORTANCE OF FUNDAMENTAL NEUROSCIENCE 717
38.4 TEMPORAL DYNAMICS IN NEUROMORPHIC ARCHITECTURES 718
38.5 SYNAPSE AND NEURON CIRCUITS 719
38.6 SPIKE-BASED MULTICHIP NEUROMORPHIC SYSTEMS 721
38.7 STATE-DEPENDENT COMPUTATION IN NEUROMORPHIC SYSTEMS 722
38.8 CONCLUSIONS 722
REFERENCES 723
39
NEUROENGINEERING
DA MIEN COYLE, RONEN SOSNIK 727
39.1 OVERVIEW - NEUROENGINEERING IN GENERAL 728
39.2 HUMAN MOTOR CONTROL 732
39.3 MODELING THE MOTOR SYSTEM - INTERNAL MOTOR MODELS 733
39.4 SENSORIMOTOR LEARNING 736
39.5 MRI AND THE MOTOR SYSTEM - STRUCTURE AND FUNCTION 738
39.6 ELECTROCORTICOGRAPHIC MOTOR CORTICAL SURFACE POTENTIALS 741
39.7 MEG AND EEG
- EXTRA CEREBRAL MAGNETIC AND ELECTRIC FIELDS
OF THE MOTOR SYSTEM 745
39.8 EXTRACELLULAR RECORDING - DECODING HAND MOVEMENTS
FROM SPIKES AND LOCAL FIELD POTENTIAL 748
39.9 TRANSLATING BRAINWAVES INTO CONTROL SIGNALS - BCIS 754
39.10 CONCLUSION 762
REFERENCES 764
40 EVOLVING CONNECTIONIST SYSTEMS:
FROM NEURO-FUZZY-, TO SPIKING- AND NEURO-GENETIC
NIKOLA KASABOV 771
40.1 PRINCIPLES OF EVOLVING CONNECTIONIST SYSTEMS (EC0S) 771
40.2 HYBRID SYSTEMS AND EVOLVING NEURO-FUZZY SYSTEMS 772
40.3 EVOLVING SPIKING NEURAL NETWORKS (ESNN) 775
40.4 COMPUTATIONAL NEURO-GENETIC MODELING (CNGM) 778
40.5 CONCLUSIONS AND FURTHER DIRECTIONS 779
REFERENCES 780
41
MACHINE LEARNING APPLICATIONS
PIERO P. BONISSONE 783
41.1 MOTIVATION 784
IFL.2 MACHINE LEARNING (ML) FUNCTIONS 786
41.3 CI/ML APPLICATIONS IN INDUSTRIAL DOMAINS:
PROGNOSTICS AND HEALTH MANAGEMENT (PHM) 787
41.4 CI/ML APPLICATIONS IN FINANCIAL DOMAINS: RISK MANAGEMENT 797
*1-1.5 MODEL ENSEMBLES AND FUSION 807
41.6 SUMMARY AND FUTURE RESEARCH CHALLENGES 812
REFERENCES 817
PART E EVOLUTIONARY COMPUTATION
42
GENETIC ALGORITHMS
JONATHAN E. ROWE 825
42.1 ALGORITHMIC FRAMEWORK 826
42.2 SELECTION METHODS 828
42.3 REPLACEMENT METHODS 831
42.4 MUTATION METHODS 832
42.5 SELECTION-MUTATION BALANCE 834
42.6 CROSSOVER METHODS 836
42.7 POPULATION DIVERSITY 838
42.8 PARALLEL GENETIC ALGORITHMS 839
42.9 POPULATIONS AS SOLUTIONS 841
42.10 CONCLUSIONS 842
REFERENCES 843
43
GENETIC PROGRAMMING
JAMES MCDERMOTT, UNA-MAY O'REILLY 845
43.1 EVOLUTIONARY SEARCH FOR EXECUTABLE PROGRAMS 845
43.2 HISTORY 846
43.3 TAXONOMY OF AL AND GP 848
43.4 USES OF GP 853
43.5 RESEARCH TOPICS 857
43.6 PRACTICALITIES 861
REFERENCES 862
44
EVOLUTION STRATEGIES
NIKOLAUS HANSEN, DIRK I/. ARNOLD, ANNE AUGER 871
44.1 OVERVIEW 871
44.2 MAIN PRINCIPLES 873
44.3 PARAMETER CONTROL 877
44.4 THEORY 886
REFERENCES 895
45
ESTIMATION OF DISTRIBUTION ALGORITHMS
MARTIN PELIKAN, MARK 1/1/. HAUSCHILD, FERNANDO G. LOBO 899
45.1 BASIC EDA PROCEDURE 900
45.2 TAXONOMY OF EDA MODELS 903
45.3 OVERVIEW OF EDAS 908
45.4 EDA THEORY 916
45.5 EFFICIENCY ENHANCEMENT TECHNIQUES FOR EDAS 917
45.6 STARTING POINTS FOR OBTAINING ADDITIONAL INFORMATION 920
45.7 SUMMARY AND CONCLUSIONS 921
REFERENCES 921
46
PARALLEL EVOLUTIONARY ALGORITHMS
DIRKSUDHOLT 929
46.1 PARALLEL MODELS 931
46.2 EFFECTS OF PARALLELIZATION 935
46.3 ON THE SPREAD OF INFORMATION IN PARALLEL EAS 938
46.4 EXAMPLES WHERE PARALLEL EAS EXCEL 943
46.5 SPEEDUPS BY PARALLELIZATION 949
46.6 CONCLUSIONS 956
REFERENCES 957
47
LEARNING CLASSIFIER SYSTEMS
MARTIN V. BUTZ 961
47.1 BACKGROUND 962
47.2 XCS 965
47.3 XCSF 970
47.4 DATA MINING 972
47.5 BEHAVIORAL LEARNING 973
47.6 CONCLUSIONS 977
47.7 BOOKS AND SOURCE CODE 978
REFERENCES 979
48
INDICATOR-BASED SELECTION
LOTHARTHIELE 983
48.1 MOTIVATION 983
48.2 BASIC CONCEPTS 984
48.3 SELECTION SCHEMES 987
48.4 PREFERENCE-BASED SELECTION 990
48.5 CONCLUDING REMARKS 992
REFERENCES 993
49
MULTI-OBJECTIVE EVOLUTIONARY ALGORITHMS
KALYANMOY DEB 995
49.1 PREAMBLE 995
49.2 EVOLUTIONARY MULTI-OBJECTIVE OPTIMIZATION (EMO) 996
49.3 A BRIEF TIMELINE FOR THE DEVELOPMENT OF EMO METHODOLOGIES 999
49.4 ELITIST EMO: NSGA-II 1000
49.5 APPLICATIONS OF EMO 1002
49.6 RECENT DEVELOPMENTS IN EMO 1004
49.7 CONCLUSIONS 1010
REFERENCES 1011
50
PARALLEL MULTIOBJECTIVE EVOLUTIONARY ALGORITHMS
FRANCISCO LUNA, ENRIQUE ALBA 1017
50.1 MULTIOBJECTIVE OPTIMIZATION AND PARALLELISM 1017
50.2 PARALLEL MODELS FOR EVOLUTIONARY MULTI-OBJECTIVE ALGORITHMS 1018
50.3 AN UPDATED REVIEW OF THE LITERATURE 1020
50.4 CONCLUSIONS AND FUTURE WORKS 1026
REFERENCES 1027
51
MANY-OBJECTIVE PROBLEMS: CHALLENGES AND METHODS
ANTONIO LOPEZ JAIMES, CARLOS A. COELLO COELLO 1033
51.1 BACKGROUND 1033
51.2 BASIC CONCEPTS AND NOTATION 1034
51.3 SOURCES OF DIFFICULTY TO SOLVE MANY-OBJECTIVE
OPTIMIZATION PROBLEMS 1036
51.4 CURRENT APPROACHES TO DEAL WITH MANY-OBJECTIVE PROBLEMS 1038
51.5 RECOMBINATION OPERATORS AND MATING RESTRICTIONS 1042
51.6 SCALARIZATION METHODS 1043
51.7 CONCLUSIONS AND RESEARCH PATHS 1043
REFERENCES 1044
52
MEMETIC AND HYBRID EVOLUTIONARY ALGORITHMS
JHON EDGAR AMAYA, CARLOS COTTA PORRAS, ANTONIO J. FERNANDEZ LEIVA 1047
52.1 OVERVIEW 1047
52.2 A BIRD'S VIEW OF EVOLUTIONARY ALGORITHMS 1049
52.3 FROM HYBRID METAHEURISTICS TO HYBRID EAS 1050
52.4 MEMETIC ALGORITHMS 1052
52.5 COOPERATIVE OPTIMIZATION MODELS 1055
52.6 CONCLUSIONS 1056
REFERENCES 1056
53
DESIGN OF REPRESENTATIONS AND SEARCH OPERATORS
FRANZ ROTHLAUF 1061
53.1 REPRESENTATIONS 1061
53.2 SEARCH OPERATORS 1065
53.3 PROBLEM-SPECIFIC DESIGN OF REPRESENTATIONS AND SEARCH OPERATORS.
1071
53.4 SUMMARY AND CONCLUSIONS 1079
REFERENCES 1080
54
STOCHASTIC LOCAL SEARCH ALGORITHMS: AN OVERVIEW
HOLGERH. HOOS, THOMAS STUTZLE 1085
54.1 THE NATURE AND CONCEPT OF SLS 1086
54.2 GREEDY CONSTRUCTION HEURISTICS AND ITERATIVE IMPROVEMENT 1089
54.3 SIMPLE SLS METHODS 1091
54.4 HYBRID SLS METHODS 1094
54.5 POPULATION-BASED SLS METHODS 1095
54.6 RECENT RESEARCH DIRECTIONS 1097
REFERENCES 1100
55
PARALLEL EVOLUTIONARY COMBINATORIAL OPTIMIZATION
EL-GHAZALI TALBI 1107
55.1 MOTIVATION 1107
55.2 PARALLEL DESIGN OF EAS 1108
55.B PARALLEL IMPLEMENTATION OF EAS 1113
55.4 PARALLEL EAS UNDER PARADISEO 1122
55.5 CONCLUSIONS AND PERSPECTIVES 1123
REFERENCES 1124
56
HOW TO CREATE GENERALIZABLE RESULTS
THOMAS BARTZ-BEIELSTEIN 1127
56.1 TEST PROBLEMS IN COMPUTATIONAL INTELLIGENCE 1127
56.2 FEATURES OF OPTIMIZATION PROBLEMS 1128
56.3 ALGORITHM FEATURES 1130
56.^ OBJECTIVE FUNCTIONS 1131
56.5 CASE STUDIES 1133
56.6 SUMMARY AND OUTLOOK 1141
REFERENCES 1142
57
COMPUTATIONAL INTELLIGENCE IN INDUSTRIAL APPLICATIONS
EKATERINA VLADISLAVLEVA, GUIDO SMITS, MARK KOTANCHEK 1143
57.1 INTELLIGENCE AND COMPUTATION 1143
57.2 COMPUTATIONAL MODELING FOR PREDICTIVE ANALYTICS 1144
57.3 METHODS 1147
57.4 WORKFLOWS 1149
57.5 EXAMPLES 1150
57.6 CONCLUSIONS 1155
REFERENCES 1156
58
SOLVING PHASE EQUILIBRIUM PROBLEMS
BY MEANS OF AVOIDANCE-BASED MULTIOBJECTIVIZATION
MIKE PREUSS, SIMON WESSING, GUNTER RUDOLPH, GABRIEIE SADOWSKI 1159
58.1 COPING WITH REAL-WORLD OPTIMIZATION PROBLEMS 1159
58.2 THE PHASE-EQUILIBRIUM CALCULATION PROBLEM 1161
58.3 MULTIOBJECTIVIZATION-ASSISTED MULTIMODAL OPTIMIZATION: M0AM0.
1162
58.4 SOLVING GENERAL PHASE-EQUILIBRIUM PROBLEMS 1165
58.5 CONCLUSIONS AND OUTLOOK 1169
REFERENCES 1169
59
MODELING AND OPTIMIZATION OF MACHINING PROBLEMS
DIRK BIERMANN, PETRA KERSTING, TOBIAS WAGNER, ANDREAS ZABEL 1173
59.1 ELEMENTS OF A MACHINING PROCESS 1174
59.2 DESIGN OPTIMIZATION 1175
59.3 COMPUTER-AIDED DESIGN AND MANUFACTURING 1176
59.4 MODELING AND SIMULATION OF THE MACHINING PROCESS 1177
59.5 OPTIMIZATION OF THE PROCESS PARAMETERS 1178
59.6 PROCESS MONITORING 1179
59.7 VISUALIZATION 1179
59.8 SUMMARY AND OUTLOOK 1180
REFERENCES 1180
60
AERODYNAMIC DESIGN WITH PHYSICS-BASED SURROGATES
EMILIANO LULIANO, DOMENICO QUAGLIARELLA 1185
60.1 THE AERODYNAMIC DESIGN PROBLEM 1186
60.2 LITERATURE REVIEW OF SURROGATE-BASED OPTIMIZATION 1187
60.3 POD-BASED SURROGATES 1190
60.APPLICATION EXAMPLE
OF POD-BASED
SURROGATES.: 1191
60.5 STRATEGIES FOR IMPROVING POD MODEL QUALITY: ADAPTIVE SAMPLING.
1199
60.6 AERODYNAMIC SHAPE OPTIMIZATION BY SURROGATE MODELING
AND EVOLUTIONARY COMPUTING 1201
60.7 CONCLUSIONS 1207
REFERENCES 1208
61 KNOWLEDGE DISCOVERY IN BIOINFORMATICS
JULIE HAMON, JULIE JACQUES, LAETITIA JOURDAN, CLARISSE DHAENENS 1211
61.1 CHALLENGES IN BIOINFORMATICS 1211
61.2 ASSOCIATION RULES BY EVOLUTIONARY ALGORITHM IN BIOINFORMATICS 1212
61.3 FEATURE SELECTION FOR CLASSIFICATION AND REGRESSION
BY EVOLUTIONARY ALGORITHM IN BIOINFORMATICS 1215
61.^ CLUSTERING BY EVOLUTIONARY ALGORITHM IN BIOINFORMATICS 1218
61.5 CONCLUSION 1220
REFERENCES 1221
62
INTEGRATION OF METAHEURISTICS AND CONSTRAINT PROGRAMMING
LUCA DI GASPERO 1225
62.1 CONSTRAINT PROGRAMMING AND METAHEURISTICS 1225
62.2 CONSTRAINT PROGRAMMING ESSENTIALS 1226
62.3 INTEGRATION OF METAHEURISTICS AND CP 1230
62.4 CONCLUSIONS 1234
REFERENCES 1235
63
GRAPH COLORING AND RECOMBINATION
RHYD LEWIS 1239
63.1 GRAPH COLORING 1239
63.2 ALGORITHMS FOR GRAPH COLORING 1240
63.3 SETUP 1244
63.4 EXPERIMENT 1 1246
63.5 EXPERIMENT 2 1249
63.6 CONCLUSIONS AND DISCUSSION 1251
REFERENCES 1252
64
METAHEURISTIC ALGORITHMS AND TREE DECOMPOSITION
THOMAS HAMMERL, NYSRET MUSLIU, WERNER SCHAFHAUSER 1255
64.1 TREE DECOMPOSITIONS 1256
64.2 GENERATING TREE DECOMPOSITIONS BY METAHEURISTIC TECHNIQUES 1258
64.3 CONCLUSION 1268
REFERENCES 1269
65 EVOLUTIONARY COMPUTATION AND CONSTRAINT SATISFACTION
JANO I. VAN HEMERT 1271
65.1 INFORMAL INTRODUCTION TO CSP 1271
65.2 FORMAL DEFINITIONS 1272
65.3 SOLVING CSP WITH EVOLUTIONARY ALGORITHMS 1273
65.4 PERFORMANCE INDICATORS 1275
65.5 SPECIFIC CONSTRAINT SATISFACTION PROBLEMS 1277
65.6 CREATING RATHER THAN SOLVING PROBLEMS 1283
65.7 CONCLUSIONS AND FUTURE DIRECTIONS 1284
REFERENCES 1284
PART F SWARM INTELLIGENCE
66 SWARM INTELLIGENCE IN OPTIMIZATION AND ROBOTICS
CHRISTIAN BLUM, RODERICH OROFI 1291
66.1 OVERVIEW 1291
66.2 SI IN OPTIMIZATION 1292
66.3 SI IN ROBOTICS: SWARM ROBOTICS 1296
66.4 RESEARCH CHALLENGES 1302
REFERENCES 1303
67
PREFERENCE-BASED MULTIOBJECTIVE
PARTICLE SWARM OPTIMIZATION FOR AIRFOIL DESIGN
ROBERT CARRESE, XIAODONG LI 1311
67.1 AIRFOIL DESIGN 1311
67.2 SHAPE PARAMETERIZATION AND FLOW SOLVER 1317
67.3 OPTIMIZATION ALGORITHM 1319
67.4 CASE STUDY: AIRFOIL SHAPE OPTIMIZATION 1323
67.5 CONCLUSION 1329
REFERENCES 1329
68 ANT COLONY OPTIMIZATION FOR THE MINIMUM-WEIGHT ROOTED
ARBORESCENCE PROBLEM
CHRISTIAN BLUM, SERGI MATEO BELLIDO 1333
68.1 INTRODUCTORY REMARKS 1333
68.2 THE MINIMUM-WEIGHT ROOTED ARBORESCENCE PROBLEM 1334
68.3
DP-HEUR
: A HEURISTIC APPROACH TO THE MWRA PROBLEM 1335
68.4 ANT COLONY OPTIMIZATION FOR THE MWRA PROBLEM 1335
68.5 EXPERIMENTAL EVALUATION 1337
68.6 CONCLUSIONS AND FUTURE WORK 1343
REFERENCES 1343
69 AN
INTELLIGENT SWARM OF MARKOVIAN AGENTS
DARIO BRUNEO, MARCO SCARPA, ANDREA BOBBIO, DAVIDE CEROTTI,
MARCO GRIBAUDO 1345
69.1 SWARM INTELLIGENCE: A MODELING PERSPECTIVE 1345
69.2 MARKOVIAN AGENT MODELS 1346
69.3 A CONSOLIDATED EXAMPLE: WSN ROUTING 1349
69.IF ANT COLONY OPTIMIZATION 1354
69.5 CONCLUSIONS 1358
REFERENCES 1359
70
HONEY BEE SOCIAL FORAGING ALGORITHM FOR RESOURCE ALLOCATION
JAIRO ALONSO GIRALDO, NICANOR QUIJANO, KEVIN M. PASSINO 1361
70.1 HONEY BEE FORAGING ALGORITHM 1363
70.2 APPLICATION IN A MULTIZONE TEMPERATURE CONTROL GRID 1365
70.3 RESULTS 1371
70.4 DISCUSSION 1373
70.5 CONCLUSIONS 1374
REFERENCES 1374
71
FUNDAMENTAL COLLECTIVE BEHAVIORS IN SWARM ROBOTICS
VITO TRIANNI, ALEXANDRE CAMPO 1377
71.1 DESIGNING SWARM BEHAVIOURS 1378
71.2 GETTING TOGETHER: AGGREGATION 1379
71.3 ACTING TOGETHER: SYNCHRONIZATION 1381
71.4 STAYING TOGETHER: COORDINATED MOTION 1383
71.5 SEARCHING TOGETHER: COLLECTIVE EXPLORATION 1386
71.6 DECIDING TOGETHER: COLLECTIVE DECISION MAKING 1388
71.7 CONCLUSIONS 1390
REFERENCES 1391
72
COLLECTIVE MANIPULATION AND CONSTRUCTION
LYNNE PARKER 1395
72.1 OBJECT TRANSPORTATION 1395
72.2 OBJECT SORTING AND CLUSTERING 1401
72.3 COLLECTIVE CONSTRUCTION AND WALL BUILDING 1402
72.4 CONCLUSIONS 1404
REFERENCES 1404
73
RECONFIGURABLE ROBOTS
KASPER ST0Y 1407
73.1 MECHATRONICS SYSTEM INTEGRATION 1409
73.2 CONNECTION MECHANISMS 1410
73.3 ENERGY 1411
73.4 DISTRIBUTED CONTROL 1412
73.5 PROGRAMMABILITY AND DEBUGGING 1417
73.6 PERSPECTIVE 1418
73.7 FURTHER READING 1419
REFERENCES 1419
74
PROBABILISTIC MODELING OF SWARMING SYSTEMS
NIKOLA US CORRELL, HEIKO HAMANN 1423
74.1 FROM BIOLIGICAL TO ARTIFICIAL SWARMS 1423
74.2 THE MASTER EQUATION 1424
74.3 NON-SPATIAL PROBABILISTIC MODELS 1424
74.4 SPATIAL MODELS: COLLECTIVE OPTIMIZATION 1428
74.5 CONCLUSION 1431
REFERENCES 1431
PART G HYBRID SYSTEMS
75 A ROBUST EVOLVING CLOUD-BASED CONTROLLER
PLAMEN P. ANGELOV, IGOR SKRJANC, SASO BLAZIC 1435
75.1 OVERVIEW OF SOME ADAPTIVE AND EVOLVING CONTROL APPROACHES 1435
75.2 STRUCTURE OF THE CLOUD-BASED CONTROLLER 1437
75.3 EVOLVING METHODOLOGY FOR RECCO 1439
75.4 SIMULATION STUDY 1442
75.5 CONCLUSIONS 1447
REFERENCES 1448
76
EVOLVING EMBEDDED FUZZY CONTROLLERS
OSCAR H. MONTIEL ROSS, ROBERTO SEPULVEDA CRUZ 1451
76.1 OVERVIEW 1452
76.2 TYPE-1 AND TYPE-2 FUZZY CONTROLLERS 1454
76.3 HOST TECHNOLOGY 1457
76.4 HARDWARE IMPLEMENTATION APPROACHES 1458
76.5 DEVELOPMENT OF A STANDALONE IT2FC 1461
76.6 DEVELOPING OF IT2FC COPROCESSORS 1466
76.7 IMPLEMENTING A GA IN AN FPGA 1468
76.8 EVOLVING FUZZY CONTROLLERS 1470
REFERENCES 1475
77
MULTIOBJECTIVE GENETIC FUZZY SYSTEMS
HISAO ISHIBUCHI, YUSUKE NOJIMA 1479
77.1 FUZZY SYSTEM DESIGN 1479
77.2 ACCURACY MAXIMIZATION 1482
77.3 COMPLEXITY MINIMIZATION 1487
77.4 SINGLE-OBJECTIVE APPROACHES 1489
77.5 EVOLUTIONARY MULTIOBJECTIVE APPROACHES 1491
77.6 CONCLUSION 1494
REFERENCES 1494
78
BIO-INSPIRED OPTIMIZATION OF TYPE-2 FUZZY CONTROLLERS
OSCAR CASTILLO 1499
78.1 RELATED WORK IN TYPE-2 FUZZY CONTROL 1499
78.2 FUZZY LOGIC SYSTEMS 1500
78.3 BIO-INSPIRED OPTIMIZATION METHODS 1503
78.4 GENERAL OVERVIEW OF THE AREA AND FUTURE TRENDS 1505
78.5 CONCLUSIONS 1506
REFERENCES 1506
79 PATTERN RECOGNITION WITH MODULAR NEURAL NETWORKS
AND TYPE-2 FUZZY LOGIC
PATRICIA MELIN 1509
79.1 RELATED WORK IN THE AREA 1509
79.2 OVERVIEW OF FUZZY EDGE DETECTORS 1510
79.3 EXPERIMENTAL SETUP 1512
79.^ EXPERIMENTAL RESULTS 1513
79.5 CONCLUSIONS 1515
REFERENCES 1515
80 FUZZY CONTROLLERS FOR AUTONOMOUS MOBILE ROBOTS
PATRICIA MELIN, OSCAR CASTILLO 1517
80.1 FUZZY CONTROL OF MOBILE ROBOTS 1517
80.2 THE CHEMICAL OPTIMIZATION PARADIGM 1518
80.3 THE MOBILE ROBOT 1521
80.4 FUZZY LOGIC CONTROLLER 1522
80.5 EXPERIMENTAL RESULTS 1523
80.6 CONCLUSIONS 1530
REFERENCES 1530
81 BIO-INSPIRED OPTIMIZATION METHODS
FEVRIER VALDEZ 1533
81.1 BIO-INSPIRED METHODS 1533
81.2 BIO-INSPIRED OPTIMIZATION METHODS 1533
81.3 A BRIEF HISTORY OF GPUS 1535
81.4 EXPERIMENTAL RESULTS 1535
81.5 CONCLUSIONS 1538
REFERENCES 1538
ACKNOWLEDGEMENTS
1539
ABOUT THE AUTHORS 1543
DETAILED CONTENTS
1569
INDEX
1605 |
| any_adam_object | 1 |
| author2 | Kacprzyk, Janusz 1947- |
| author2_role | edt |
| author2_variant | j k jk |
| author_GND | (DE-588)110363248 |
| author_facet | Kacprzyk, Janusz 1947- |
| building | Verbundindex |
| bvnumber | BV042595136 |
| classification_rvk | ST 300 |
| ctrlnum | (OCoLC)881386996 (DE-599)DNB105180003X |
| 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 | Maschinenbau / Maschinenwesen Informatik |
| format | Book |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>00000nam a22000008c 4500</leader><controlfield tag="001">BV042595136</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">20160217</controlfield><controlfield tag="007">t</controlfield><controlfield tag="008">150602s2015 gw ad|| |||| 00||| eng d</controlfield><datafield tag="015" ind1=" " ind2=" "><subfield code="a">14,N24</subfield><subfield code="2">dnb</subfield></datafield><datafield tag="016" ind1="7" ind2=" "><subfield code="a">105180003X</subfield><subfield code="2">DE-101</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">3662435047</subfield><subfield code="9">3-662-43504-7</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9783662435045</subfield><subfield code="c">Gb. : EUR 373.43 (DE) (freier Pr.), EUR 383.90 (AT) (freier Pr.), sfr 465.00 (freier Pr.)</subfield><subfield code="9">978-3-662-43504-5</subfield></datafield><datafield tag="024" ind1="3" ind2=" "><subfield code="a">9783662435045</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">Best.-Nr.: 80022299</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)881386996</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DNB105180003X</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">rakddb</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="044" ind1=" " ind2=" "><subfield code="a">gw</subfield><subfield code="c">XA-DE-BE</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-1050</subfield><subfield code="a">DE-210</subfield><subfield code="a">DE-12</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">006.3</subfield><subfield code="2">22/ger</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">ST 300</subfield><subfield code="0">(DE-625)143650:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">620</subfield><subfield code="2">sdnb</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Springer handbook of computational intelligence</subfield><subfield code="c">Janusz Kacprzyk ... (Eds.)</subfield></datafield><datafield tag="246" ind1="1" ind2="3"><subfield code="a">Handbook of computational intelligence</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Berlin [u.a.]</subfield><subfield code="b">Springer</subfield><subfield code="c">2015</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">LVI, 1633 S.</subfield><subfield code="b">Ill., graph. Darst.</subfield><subfield code="c">25 cm</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Soft Computing</subfield><subfield code="0">(DE-588)4455833-8</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="655" ind1=" " ind2="7"><subfield code="0">(DE-588)4143413-4</subfield><subfield code="a">Aufsatzsammlung</subfield><subfield code="2">gnd-content</subfield></datafield><datafield tag="689" ind1="0" ind2="0"><subfield code="a">Soft Computing</subfield><subfield code="0">(DE-588)4455833-8</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2=" "><subfield code="5">DE-604</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Kacprzyk, Janusz</subfield><subfield code="d">1947-</subfield><subfield code="0">(DE-588)110363248</subfield><subfield code="4">edt</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Online-Ausgabe</subfield><subfield code="z">978-3-662-43505-2</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="m">X:MVB</subfield><subfield code="q">text/html</subfield><subfield code="u">http://deposit.dnb.de/cgi-bin/dokserv?id=4682141&prov=M&dok_var=1&dok_ext=htm</subfield><subfield code="3">Inhaltstext</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="m">DNB Datenaustausch</subfield><subfield code="q">application/pdf</subfield><subfield code="u">http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028028304&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA</subfield><subfield code="3">Inhaltsverzeichnis</subfield></datafield><datafield tag="943" ind1="1" ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-028028304</subfield></datafield></record></collection> |
| genre | (DE-588)4143413-4 Aufsatzsammlung gnd-content |
| genre_facet | Aufsatzsammlung |
| id | DE-604.BV042595136 |
| illustrated | Illustrated |
| indexdate | 2024-09-10T01:44:31Z |
| institution | BVB |
| isbn | 3662435047 9783662435045 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-028028304 |
| oclc_num | 881386996 |
| open_access_boolean | |
| owner | DE-1050 DE-210 DE-12 |
| owner_facet | DE-1050 DE-210 DE-12 |
| physical | LVI, 1633 S. Ill., graph. Darst. 25 cm |
| publishDate | 2015 |
| publishDateSearch | 2015 |
| publishDateSort | 2015 |
| publisher | Springer |
| record_format | marc |
| spelling | Springer handbook of computational intelligence Janusz Kacprzyk ... (Eds.) Handbook of computational intelligence Berlin [u.a.] Springer 2015 LVI, 1633 S. Ill., graph. Darst. 25 cm txt rdacontent n rdamedia nc rdacarrier Soft Computing (DE-588)4455833-8 gnd rswk-swf (DE-588)4143413-4 Aufsatzsammlung gnd-content Soft Computing (DE-588)4455833-8 s DE-604 Kacprzyk, Janusz 1947- (DE-588)110363248 edt Erscheint auch als Online-Ausgabe 978-3-662-43505-2 X:MVB text/html http://deposit.dnb.de/cgi-bin/dokserv?id=4682141&prov=M&dok_var=1&dok_ext=htm Inhaltstext DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028028304&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Springer handbook of computational intelligence Soft Computing (DE-588)4455833-8 gnd |
| subject_GND | (DE-588)4455833-8 (DE-588)4143413-4 |
| title | Springer handbook of computational intelligence |
| title_alt | Handbook of computational intelligence |
| title_auth | Springer handbook of computational intelligence |
| title_exact_search | Springer handbook of computational intelligence |
| title_full | Springer handbook of computational intelligence Janusz Kacprzyk ... (Eds.) |
| title_fullStr | Springer handbook of computational intelligence Janusz Kacprzyk ... (Eds.) |
| title_full_unstemmed | Springer handbook of computational intelligence Janusz Kacprzyk ... (Eds.) |
| title_short | Springer handbook of computational intelligence |
| title_sort | springer handbook of computational intelligence |
| topic | Soft Computing (DE-588)4455833-8 gnd |
| topic_facet | Soft Computing Aufsatzsammlung |
| url | http://deposit.dnb.de/cgi-bin/dokserv?id=4682141&prov=M&dok_var=1&dok_ext=htm http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028028304&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT kacprzykjanusz springerhandbookofcomputationalintelligence AT kacprzykjanusz handbookofcomputationalintelligence |