Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
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| Format: | Buch |
| Sprache: | English |
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Upper Saddle River, NJ
Prentice Hall
1996
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| Schriftenreihe: | Neural networks, fuzzy logic
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| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XVI, 797 S. graph. Darst. 1 Diskette (9 cm) |
| ISBN: | 0132351692 |
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MARC
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| 245 | 1 | 0 | |a Neural fuzzy systems |b a neuro-fuzzy synergism to intelligent systems |c Chin-Teng Lin ; C.S. George Lee |
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Datensatz im Suchindex
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| adam_text | NEURAL FUZZY SYSTEMS A NEURO-FUZZY SYNERGISM TO INTELLIGENT SYSTEMS
CHIN-TENG LIN DEPARTMENT OF CONTROL ENGINEERING NATIONAL CHIAO-TUNG
UNIVERSITY HSINCHU, TAIWAN C.S.GEORGE LEE SCHOOL OF ELECTRICAL AND
COMPUTER ENGINEERING PURDUE UNIVERSITY WEST LAFAYETTE, INDIANA FOR BOOK
AND BOOKSTORE INFORMATION HTTP://WWW.PRENHALL.COM PRENTICE HALL PTR,
UPPER SADDLE RIVER, NJ 07458 CONTENTS PREFACE XIII 1 INTRODUCTION 1 1.1
MOTIVATION 1 1.2 FUZZY SYSTEMS 3 1.3 NEURAL NETWORKS 4 1.4 FUZZY NEURAL
INTEGRATED SYSTEMS 7 1.5 ORGANIZATION OF THE BOOK 8 1.6 REFERENCES 9
PART I FUZZY SYSTEMS 2 BASICS OF FUZZY SETS 10 2. 1 FUZZY SETS AND
OPERATION ON FUZZY SETS 10 2.2 EXTENSIONS OF FUZZY SET CONCEPTS 22 2.2.1
OTHER KINDS OF FUZZY SETS, 22 . 2.2.2 FURTHER OPERATIONS ON FUZZY
SETS, 23 2.3 EXTENSION PRINCIPLE AND ITS APPLICATIONS 29 2.3.1
OPERATIONS OFTYPE-2 FUZZY SETS, 31 2.3.2 CONSISTENCY DEGREE OF TWO FUZZY
SETS, 32 2.4 CONCLUDING REMARKS 33 2.5 PROBLEMS 34 3 FUZZY RELATIONS 37
3.1 BASICS OF FUZZY RELATIONS 37 3.2 OPERATIONS ON FUZZY RELATIONS 41
3.3 VARIOUS TYPES OF BINARY FUZZY RELATIONS 47 3.3.1 SIMILARITY
RELATIONS, 49 3.3.2 RESEMBLANCE RELATIONS, 51 3.3.3 FUZZY PARTIAL
ORDERING, 52 3.4 FUZZY RELATION EQUATIONS 54 3.5 CONCLUDING REMARKS 59
3.6 PROBLEMS 60 4 FUZZY MEASURES 63 4.1 FUZZY MEASURES 64 4.1.1 BELIEF
AND PLAUSIBILITY MEASURES, 65 4.1.2 PROBABILITY MEASURES, 72 4.1.3
POSSIBILITY ARID NECESSITY MEASURES, 74 4.2 FUZZY INTEGRALS 80 4.3
MEASURES OF FUZZINESS 83 4.4 CONCLUDING REMARKS 85 4.5 PROBLEMS 86 5
POSSIBILITY THEORY AND FUZZY ARITHMETIC 89 5.1 BASICS OF POSSIBILITY
THEORY 89 5.2 FUZZY ARITHMETIC 93 5.2.1 INTERVAL REPRESENTATION OF
UNCERTAIN VALUES, 94 5.2.2 OPERATIONS AND PROPERTIES OF FUZZY NUMBERS,
97 5.2.3 ORDERING OF FUZZY NUMBERS, 108 5.3 CONCLUDING REMARKS 111 5.4
PROBLEMS 111 6 FUZZY LOGIC AND APPROXIMATE REASONING 114 6.1 LINGUISTIC
VARIABLES 114 6.2 FUZZY LOGIC 118 6.2.1 TRUTH VALUES AND TRUTH TABLES IN
FUZZY LOGIC, 119 6.2.2 FUZZY PROPOSITIONS, 121 6.3 APPROXIMATE REASONING
123 6.3.1 CATEGORICAL REASONING, 123 6.3.2 QUALITATIVE REASONING, 126
6.3.3 SYLLOGISTIC REASONING, 127 6.3.4 DISPOSITIONAL REASONING, 129 6.4
FUZZY EXPERT SYSTEMS 131 6.4.1 MILORD, 132 6.4.2 Z-II, 136 6.5
CONCLUDING REMARKS 139 6.6 PROBLEMS 140 VI CONTENTS 7 FUZZY LOGIC
CONTROL SYSTEMS 142 7.1 BASIC STRUCTURE AND OPERATION OF FUZZY LOGIC
CONTROL SYSTEMS 142 7.1.1 INPUT-OUTPUT SPACES, 143 7.1.2 FUZZIFIER, 145
7.1.3 FUZZY RULE BASE, 145 7.1.4 INFERENCE ENGINE, 145 7.1.5
DEFUZZIFIER, 156 7.2 DESIGN METHODOLOGY OF FUZZY CONTROL SYSTEMS 159 7.3
STABILITY ANALYSIS OF FUZZY CONTROL SYSTEMS 166 7.4 APPLICATIONS OF
FUZZY CONTROLLERS 172 7.5 CONCLUDING REMARKS 175 7.6 PROBLEMS, 177 8
APPLICATIONS OF FUZZY THEORY 180 8.1 FUZZY PATTERN RECOGNITION 180 8.1.1
CLASSIFICATION METHODS BASED ON FUZZY RELATIONS, 182 8.1.2 FUZZY
CLUSTERING, 186 8.2 FUZZY MATHEMATICAL PROGRAMMING 190 8.3 FUZZY
DATABASES 193 8.3.1 FUZZY RELATIONAL DATABASES, 194 8.3.2 FUZZY
OBJECT-ORIENTED DATABASES, 196 8.4 HUMAN-MACHINE INTERACTIONS 199 8.5
CONCLUDING REMARKS 202 8.6 PROBLEMS 203 ARTIFICIAL NEURAL NETWORKS
INTRODUCTION TO ARTIFICIAL NEURAL NETWORKS 205 9.1 FUNDAMENTAL CONCEPTS
OF ARTIFICIAL NEURAL NETWORKS 205 9.2 BASIC MODELS AND LEARNING RULES OF
ANNS 207 9.2.1 PROCESSING ELEMENTS, 207 5 9.2.2 CONNECTIONS, 211 9.2.3
LEARNING RULES, 212 9.3 DISTRIBUTED REPRESENTATIONS 217 9.4 CONCLUDING
REMARKS 221 9.5 PROBLEMS 221 10 FEEDFORWARD NETWORKS AND SUPERVISED
LEARNING 224 10.1 SINGLE-LAYER PERCEPTRON NETWORKS 224 10.1.1 PERCEPTRON
LEARNING RULE, 225 - 10.1.2 ADALINE,231 10.2 MULTILAYER FEEDFORWARD
NETWORKS 235 10.2.1 BACK PROPAGATION, 236 10.2.2 LEARNING FACTORS OF
BACK PROPAGATION, 244 10.2.3 TIME-DELAY NEURAL NETWORKS, 250 CONTENTS
VII 10.3 OTHER FEEDFORWARD NETWORKS 253 10.3.1 FUNCTIONAL-LINK NETWORKS,
253 10.3.2 TREE NEURAL NETWORKS, 254 10.3.3 WAVELET NEURAL NETWORKS, 255
10.4 CONCLUDING REMARKS 256 10.5 PROBLEMS 257 11 SINGLE-LAYER FEEDBACK
NETWORKS AND ASSOCIATIVE MEMORIES 263 11.1 HOPFIELD NETWORKS 263 11.1.1
DISCRETE HOPFIELD NETWORKS, 263 11.1.2 CONTINUOUS HOPFIELD NETWORKS, 267
11.2 ASSOCIATIVE MEMORIES 272 11.2.1 RECURRENT AUTOASSOCIATIVE MEMORY *
HOPFIELD MEMORY, 273 11.2.2 BIDIRECTIONAL ASSOCIATIVE MEMORY, 277 11.2.3
TEMPORAL ASSOCIATIVE MEMORY, 282 11.3 OPTIMIZATION PROBLEMS 284 11.3.1
HOPFIELD NETWORKS FOR OPTIMIZATION PROBLEMS, 284 11.3.2 BOLTZMANN
MACHINES, 291 11.4 CONCLUDING REMARKS 296 11.5 PROBLEMS 298 12
UNSUPERVISED LEARNING NETWORKS 301 12.1 UNSUPERVISED LEARNING RULES 301
12.1.1 SIGNAL HEBBIAN LEARNING RULE, 302 12.1.2 COMPETITIVE LEARNING
RULE, 304 12.1.3 DIFFERENTIAL HEBBIAN LEARNING RULE, 308 12.1.4
DIFFERENTIAL COMPETITIVE LEARNING RULE, 309 12.2 HAMMING NETWORKS 309
12.3 SELF-ORGANIZING FEATURE MAPS 311 12.4 ADAPTIVE RESONANCE THEORY 314
12.4.1 THE INSTAR-OUTSTAR MODEL * SHUNTING ACTIVATION EQUATIONS, 315
12.4.2 ADAPTIVE RESONANCE THEORY, 321 12.5 COUNTERPROPAGATION NETWORKS
326 12.6 RADIAL BASIS FUNCTION NETWORKS 328 12.7 ADAPTIVE BIDIRECTIONAL
ASSOCIATIVE MEMORIES 331 ) 12.8 HIERARCHICAL NETWORKS*NEOCOGNITRON 332
12.9 CONCLUDING REMARKS 336 12.10 PROBLEMS 337 13 RECURRENT NEURAL
NETWORKS 340 13.1 FEEDBACK BACKPROPAGATION NETWORKS 341 13.1.1 RECURRENT
BACKPROPAGATION NETWORKS, 341 13.1.2 PARTIALLY RECURRENT NETWORKS, 345
13.2 FULLY RECURRENT NETWORKS 349 13.2.1 REAL-TIME RECURRENT LEARNING,
350 VIII CONTENTS 13.2.2 TIME-DEPENDENT RECURRENT BACKPROPAGATION, 353
13.2.3 SECOND-ORDER RECURRENT NETWORKS, 357 13.2.4 THE EXTENDED KALMAN
FILTER, 363 13.3 REINFORCEMENT LEARNING 367 13.3.1 ASSOCIATIVE
REWARD-PENALTY, 368 13.3.2 REINFORCE ALGORITHMS, 371 13.3.3 TEMPORAL
DIFFERENCE METHODS, 373 13.4 CONCLUDING REMARKS 379 13.5 PROBLEMS 380 14
GENETIC ALGORITHMS 382 14.1 BASICS OF GENETIC ALGORITHMS 382 14.2
FURTHER EVOLUTION OF GENETIC ALGORITHMS 393 14.2.1 IMPROVED SELECTION
SCHEMES, 393 14.2.2 ADVANCED OPERATORS, 394 14.3 HYBRID GENETIC
ALGORITHMS 398 14.4 APPLICATIONS OF GENETIC ALGORITHMS 399 14.4.1
GENETIC ALGORITHMS FOR NEURAL NETWORK PARAMETER LEARNING, 399 14.4.2
GENETIC ALGORITHMS FOR PATH PLANNING, 404 14.4.3 GENETIC ALGORITHMS FOR
SYSTEM IDENTIFICATION AND CONTROLS, 405 14.5 GENETIC PROGRAMMING 406
14.6 CONCLUDING REMARKS 411 14.7 PROBLEMS 411 15 STRUCTURE-ADAPTIVE
NEURAL NETWORKS 414 15.1 SIMULATED EVOLUTION FOR NEURAL NETWORK
STRUCTURE LEARNING 414 15.1.1 GENETIC ALGORITHMS FOR DESIGNING NEURAL
NETWORKS, 414 15.1.2 EVOLUTIONARY PROGRAMMING FOR DESIGNING NEURAL
NETWORKS, 420 15.2 PRUNING NEURAL NETWORKS 424 15.2.1 WEIGHT DECAY, 424
15.2.2 CONNECTION AND NODE PRUNING, 425 15.3 GROWING NEURAL NETWORKS 427
; ; 15.3.1 INPUT SPACE PARTITIONING, 427 15.3.2 PROTOTYPE SELECTION, 433
15.4 GROWING AND PRUNING NEURAL NETWORKS 437 15.4.1 ACTIVITY-BASED
STRUCTURAL ADAPTATION, 437 15.4.2 FUNCTION NETWORKS, 439 15.5 CONCLUDING
REMARKS 442 15.6 PROBLEMS 443 . 16 APPLICATIONS OF NEURAL NETWORKS 445
16.1 NEURALNETWORKS IN CONTROL SYSTEMS 445 16.1.1 DYNAMIC
BACKPROPAGATION FOR SYSTEM IDENTIFICATION AND CONTROL, 448 16.1.2
CEREBELLAR MODEL ARTICULATION CONTROLLER, 457 16.2 NEURAL NETWORKS IN
SENSOR PROCESSING 464 16.3 NEURAL NETWORKS IN COMMUNICATIONS 468
CONTENTS IX 16.4 NEURAL KNOWLEDGE-BASED SYSTEMS 470 16.5 CONCLUDING
REMARKS 474 16.6 PROBLEMS 475 PART III FUZZY NEURAL INTEGRATED SYSTEMS
17 INTEGRATING FUZZY SYSTEMS AND NEURAL NETWORKS 478 17.1 BASIC CONCEPT
OF INTEGRATING FUZZY SYSTEMS AND NEURAL NETWORKS 478 17.1.1 GENERAL
COMPARISONS OF FUZZY SYSTEMS AND NEURAL NETWORKS, 478 17.1.2 CHOICE OF
FUZZY SYSTEMS OR NEURAL NETWORKS, 480 17.1.3 REASONS FOR INTEGRATING
FUZZY SYSTEMS AND NEURAL NETWORKS, 481 17.2 THE EQUIVALENCE OF FUZZY
INFERENCE SYSTEMS AND NEURAL NETWORKS 482 17.2.1 FUZZY INFERENCE SYSTEMS
AS UNIVERSAL APPROXIMATORS, 483 17.2.2 EQUIVALENCE OF SIMPLIFIED FUZZY
INFERENCE SYSTEMS AND RADIAL BASIS FUNCTION NETWORKS, 487 17.2.3
STABILITY ANALYSIS OF NEURAL NETWORKS USING STABILITY CONDITIONS OF
FUZZY SYSTEMS, 489 17.3 CONCLUDING REMARKS 494 17.4 PROBLEMS 494 18
NEURAL-NETWORK-BASED FUZZY SYSTEMS 496 18.1 NEURAL REALIZATION OF BASIC
FUZZY LOGIC OPERATIONS 496 18.2 NEURAL NETWORK-BASED FUZZY LOGIC
INFERENCE 498 18.2.1 FUZZY INFERENCE NETWORKS, 498 18.2.2 FUZZY
AGGREGATION NETWORKS, 504 18.2.3 NEURAL NETWORK*DRIVEN FUZZY REASONING,
507 18.3 NEURAL NETWORK-BASED FUZZY MODELING 511 18.3.1 RULE-BASED
NEURAL FUZZY MODELING, 511 18.3.2 NEURAL FUZZY REGRESSION MODELS, 517
18.3.3 NEURAL FUZZY RELATIONAL SYSTEMS, 523 18.4 CONCLUDING REMARKS 530
18.5 PROBLEMS 531 19 NEURAL FUZZY CONTROLLERS 533 19.1 TYPES OF NEURAL
FUZZY CONTROLLERS 534 19.2 NEURAL FUZZY CONTROLLERS WITH HYBRID
STRUCTURE-PARAMETER LEARNING 535 19.2.1 FUZZY ADAPTIVE LEARNING CONTROL
NETWORK, 535 19.2.2 FUZZY BASIS FUNCTION NETWORK WITH ORTHOGONAL LEAST
SQUARES LEARNING, 545 19.3 PARAMETER LEARNING FOR NEURAL FUZZY
CONTROLLERS 551 19.3.1 NEURAL FUZZY CONTROLLERS WITH FUZZY SINGLETON
RULES, 551 19.3.2 NEURAL FUZZY CONTROLLERS WITH TSK FUZZY RULES, 556
19.3.3 FUZZY OPERATOR TUNING, 559 CONTENTS 19.4 STRUCTURE LEARNING FOR
NEURAL FUZZY CONTROLLERS 561 19.4.1 FUZZY LOGIC RULE EXTRACTION FROM
NUMERICAL TRAINING DATA, 562 19.4.2 GENETIC ALGORITHMS FOR FUZZY
PARTITION OF INPUT SPACE, 567 19.5 ON-LINE STRUCTURE ADAPTIVE NEURAL
FUZZY CONTROLLERS 573 19.5.1 FALCON WITH ON-LINE SUPERVISED STRUCTURE
AND PARAMETER LEARNING, 573 19.5.2 FALCON WITH ART NEURAL LEARNING, 579
19.6 NEURAL FUZZY CONTROLLERS WITH REINFORCEMENT LEARNING 592 19.6.1
FALCON WITH REINFORCEMENT LEARNING, 592 19.6.2 GENERALIZED APPROXIMATE
REASONING-BASED INTELLIGENT CONTROLLER, 600 19.7 CONCLUDING REMARKS 604
19.8 PROBLEMS 605 20 FUZZY LOGIC-BASED NEURAL NETWORK MODELS 609 20.1
FUZZY NEURONS 609 20.1.1 FUZZY NEURON OF TYPE I, 610 20.1.2 FUZZY NEURON
OF TYPE II, 612 20.1.3 FUZZY NEURON OF TYPE III, 613 20.2 FUZZIFICATION
OF NEURAL NETWORK MODELS 614 20.2.1 FUZZY PERCEPTRON, 614 20.2.2 FUZZY
CLASSIFICATION WITH THE BACK-PROPAGATION NETWORK, 618 20.2.3 FUZZY
ASSOCIATIVE MEMORIES, 620 20.2.4 FUZZY ART MODELS, 626 20.2.5 FUZZY
KOHONEN CLUSTERING NETWORK, 635 20.2.6 FUZZY RCE NEURAL NETWORK, 639
20.2.7 FUZZY CEREBELLAR MODEL ARTICULATION CONTROLLER, 641 20.3 NEURAL
NETWORKS WITH FUZZY TRAINING 643 20.3.1 NEURAL NETWORKS WITH FUZZY
TEACHING INPUT, 643 20.3.2 NEURAL NETWORKS WITH FUZZY PARAMETERS, 648
20.3.3 FUZZY CONTROL FOR LEARNING PARAMETER ADAPTATION, 654 20.4
CONCLUDING REMARKS 657 20.5 PROBLEMS 658 ) 21 FUZZY NEURAL SYSTEMS FOR
PATTERN RECOGNITION 661 21.1 FUZZY NEURAL CLASSIFICATION 661 21.1.1
UNCERTAINTIES WITH TWO-CLASS FUZZY NEURAL CLASSIFICATION BOUNDARIES, 661
21.1.2 MULTILAYER FUZZY NEURAL CLASSIFICATION NETWORKS, 667 21.1.3
GENETIC ALGORITHMS FOR FUZZY CLASSIFICATION USING FUZZY RULES, 674 21.2
FUZZY NEURAL CLUSTERING 678 21.2.1 FUZZY COMPETITIVE LEARNING FOR FUZZY
CLUSTERING, 678 21.2.2 ADAPTIVE FUZZY LEADER CLUSTERING, 680 21.3 FUZZY
NEURAL MODELS FOR IMAGE PROCESSING 684 21.3.1 FUZZY SELF-SUPERVISED
MULTILAYER NETWORK FOR OBJECT EXTRACTION, 684 ^ 21.3:2 GENETIC
ALGORITHMS WITH FUZZY FITNESS FUNCTION FOR IMAGE ENHANCEMENT, 690 21A
FUZZY NEURAL NETWORKS FOR SPEECH RECOGNITION 692 CONTENTS XI 21.5
FUZZY-NEURAL HYBRID SYSTEMS FOR SYSTEM DIAGNOSIS 696 21.6 CONCLUDING
REMARKS 700 21.7 PROBLEMS 702 A MATLAB FUZZY LOGIC TOOLBOX 704 A. 1
DEMONSTRATION CASES 706 A. 1.1 A SIMPLE FUZZY LOGIC CONTROLLER, 706 A.
1.2 A NEURAL FUZZY SYSTEM*ANFIS, 707 A. 1.3 FUZZY C-MEANS CLUSTERING,
708 A.2 DEMONSTRATION OF FUZZY LOGIC APPLICATIONS 709 A. 2.1 A FUZZY
CONTROLLER FOR WATER BATH TEMPERATURE CONTROL, 709 A.2.2 A NEURAL FUZZY
CONTROLLER FOR WATER BATH TEMPERATURE CONTROL, 712 B MATLAB NEURAL
NETWORK TOOLBOX 715 B. 1 DEMONSTRATION OF VARIOUS NEURAL NETWORK MODELS
716 B.I.I HEBBIAN LEARNING RULE, 716 B.I.2 PERCEPTRON LEARNING RULE, 717
B.I.3 ADALINE, 718 B.I.4 BACK PROPAGATION, 719 B.I.5 HOPFIELD NETWORK,
722 B.I.6 INSTAR LEARNING RULE, 723 B.I.7 COMPETITIVE LEARNING RULE, 724
B.I.8 LVQ LEARNING RULE (SUPERVISED COMPETITIVE LEARNING RULE), 726
B.1.9 SELF-ORGANIZING FEATURE MAP, 726 B.I.10 RADIAL BASIS FUNCTION
NETWORK, 727 B.I.11 ELMANNETWORK, 729 B.2 DEMONSTRATION OF NEURAL
NETWORK APPLICATIONS 730 5.2.7 ADAPTIVE NOISE CANCELATION USING
THEADALINE NETWORK, 730 B.2.2 NONLINEAR SYSTEM IDENTIFICATION, 731 **
BIBLIOGRAPHY 734 INDEX 783 XII * , CONTENTS
|
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| indexdate | 2024-07-09T18:33:32Z |
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| isbn | 0132351692 |
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| publishDate | 1996 |
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| record_format | marc |
| series2 | Neural networks, fuzzy logic |
| spelling | Lin, Chin-Teng Verfasser aut Neural fuzzy systems a neuro-fuzzy synergism to intelligent systems Chin-Teng Lin ; C.S. George Lee Upper Saddle River, NJ Prentice Hall 1996 XVI, 797 S. graph. Darst. 1 Diskette (9 cm) txt rdacontent n rdamedia nc rdacarrier Neural networks, fuzzy logic Fuzzy-Logik (DE-588)4341284-1 gnd rswk-swf Neuronales Netz (DE-588)4226127-2 gnd rswk-swf Neuronales Netz (DE-588)4226127-2 s Fuzzy-Logik (DE-588)4341284-1 s DE-604 Lee, C. S. George Verfasser aut GBV Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=008691490&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Lin, Chin-Teng Lee, C. S. George Neural fuzzy systems a neuro-fuzzy synergism to intelligent systems Fuzzy-Logik (DE-588)4341284-1 gnd Neuronales Netz (DE-588)4226127-2 gnd |
| subject_GND | (DE-588)4341284-1 (DE-588)4226127-2 |
| title | Neural fuzzy systems a neuro-fuzzy synergism to intelligent systems |
| title_auth | Neural fuzzy systems a neuro-fuzzy synergism to intelligent systems |
| title_exact_search | Neural fuzzy systems a neuro-fuzzy synergism to intelligent systems |
| title_full | Neural fuzzy systems a neuro-fuzzy synergism to intelligent systems Chin-Teng Lin ; C.S. George Lee |
| title_fullStr | Neural fuzzy systems a neuro-fuzzy synergism to intelligent systems Chin-Teng Lin ; C.S. George Lee |
| title_full_unstemmed | Neural fuzzy systems a neuro-fuzzy synergism to intelligent systems Chin-Teng Lin ; C.S. George Lee |
| title_short | Neural fuzzy systems |
| title_sort | neural fuzzy systems a neuro fuzzy synergism to intelligent systems |
| title_sub | a neuro-fuzzy synergism to intelligent systems |
| topic | Fuzzy-Logik (DE-588)4341284-1 gnd Neuronales Netz (DE-588)4226127-2 gnd |
| topic_facet | Fuzzy-Logik Neuronales Netz |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=008691490&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT linchinteng neuralfuzzysystemsaneurofuzzysynergismtointelligentsystems AT leecsgeorge neuralfuzzysystemsaneurofuzzysynergismtointelligentsystems |