Uncertainty analysis in engineering and sciences: fuzzy logic, statistics, and neural network approach
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| Format: | Book |
|---|---|
| Language: | English |
| Published: |
Boston [u.a.]
Kluwer
1997 [erschienen] 1998
|
| Series: | International series in intelligent technologies
11 |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | XXIV, 370 S. graph. Darst. |
| ISBN: | 0792380304 |
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| 650 | 4 | |a Engineering |x Statistical methods | |
| 650 | 4 | |a Fuzzy logic | |
| 650 | 4 | |a Neural networks (Computer science) | |
| 650 | 4 | |a Reliability (Engineering) | |
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| adam_text | UNCERTAINTY ANALYSIS IN ENGINEERING AND SCIENCES: FUZZY LOGIC,
STATISTICS, AND NEURAL NETWORK APPROACH WITH A FOREWORD BY H.-J.
ZIMMERMANN BILAL M. AYYUB, PHD, PE UNIVERSITY OF MARYLAND, COLLEGE PARK
U.S.A. MADANM. GUPTA, PHD UNIVERSITY OF SASKATCHEWAN, SASKATOON CANADA
1997 KLUWER ACADEMIC PUBLISHERS BOSTON * DORDRECHT * LONDON CONTENTS
CONTRIBUTORS XV FOREWORD XIX HANS-JIIRGEN ZIMMERMANN PREFACE XXIII BILAL
M. AYYUB, AND MADAN M. GUPTA I. UNCERTAINTY TYPES, MODELS, AND MEASURES
CHAPTER 1. THE ROLE OF CONSTRAINED FUZZY ARITHMETIC IN ENGINEERING 1
GEORGE J. KLIR, BINGHAMTON UNIVERSITY, STATE UNIVERSITY OF NEW YORK, USA
1. INTRODUCTION 1 2. STANDARD FUZZY ARITHMETIC 3 3. CONSTRAINED FUZZY
ARITHMETIC 4 4. REQUISITE EQUALITY CONSTRAINTS 7 5. OTHER REQUISITE
CONSTRAINTS 10 6. APPLICATIONS OF FUZZY ARITHMETIC 13 7. CONCLUSIONS 15
8. REFERENCES 16 CHAPTER 2. GENERAL PERSPECTIVE ON THE FORMALIZATION OF
UNCERTAIN KNOWLEDGE 21 ELISABETH UMKEHRER, AND KERSTIN SCHILL,
UNIVERSITY OF MUNICH, GERMANY 1. INTRODUCTION 21 2. THE PROBLEM OF
UNCERTAIN KNOWLEDGE 22 3. A FORMALISM FOR UNCERTAIN KNOWLEDGE 23 3.1
KNOWLEDGE 23 3.1.1 DISTINCTION AND DESCRIPTION 23 3.1.2 CONCEPTS AND
FACTS 24 3.1.3 OBSERVATION 24 3.1.4 KNOWLEDGE 25 3.2 FORMALIZATION OF
KNOWLEDGE 25 3.3 KNOWLEDGE REPRESENTATION 26 3.3.1 DENOTATION 26 VI
3.3.2 UNCERTAINTY VALUES 26 3.4 FORMALIZATION OF KNOWLEDGE
REPRESENTATION 27 3.5 KNOWLEDGE UPDATE 30 3.6 FORMALIZATION OF KNOWLEDGE
UPDATE 31 3.7 AXIOMATIC SYSTEMS AND UNCERTAINTY THEORIES 33 4.
DISCUSSION 34 5. REFERENCES 35 CHAPTER 3. DISTRIBUTIONAL REPRESENTATIONS
OF RANDOM INTERVAL MEASUREMENTS 37 CLIFF JOSLYN, LOS ALAMOS NATIONAL
LABORATORY, NEW MEXICO, USA 1. INTRODUCTION 37 2. MATHEMATICAL
PRELIMINARIES 38 2.1 RANDOM SETS, INTERVALS, AND EVIDENCE MEASURES 38
2.2 PROBABILITY AND POSSIBILITY 39 2.3 POSSIBILISTIC-PROBABILISTIC
COMPATIBILITY 40 3. POSSIBILISTIC MEASUREMENT FROM CONSISTENT RANDOM
INTERVALS 41 3.1 PROBABILISTIC MEASUREMENT 41 3.2 RANDOM SET MEASUREMENT
AND POSSIBILISTIC HISTOGRAMS 41 3.3 REALIZATION 42 4. STRONGLY
COMPATIBLE PROBABILITY DISTRIBUTIONS 47 5. FREQUENCY DISTRIBUTIONS FROM
EMPIRICAL RANDOM SETS 47 6. REFERENCES 50 CHAPTER 4. A FUZZY MORPHOLOGY:
A LOGICAL APPROACH 53 BERNARD DE BAETS, UNIVERSITY OF GENT, BELGIUM 1.
INTRODUCTION 53 2. BINARY MORPHOLOGY 54 3. FUZZY LOGICAL OPERATORS 55 4.
FUZZY MORPHOLOGY 57 5. BASIC PRINCIPLES 60 6. ELEMENTARY PROPERTIES 61
7. IDEMPOTENCE 64 8. REFERENCES 66 VLL II. APPLICATIONS TO ENGINEERING
SYSTEMS CHAPTER 5. RELIABILITY ANALYSIS WITH FUZZINESS AND RANDOMNESS 69
RU-JEN CHAO, AND BILAL M. AYYUB, UNIVERSITY OF MARYLAND AT COLLEGE PARK,
USA 1. UNCERTAINTY TYPES 69 2. MERGING COGNITIVE UNCERTAINTY INTO
NON-COGNITIVE UNCERTAINTY 70 3. FUZZY-RANDOM MOMENTS 73 4. METHODS FOR
COMPUTING MOMENTS OF FUZZY-RANDOM VARIABLES 74 4.1 MOMENTS METHOD 75 4.2
DISCRETE METHOD 75 5. SIMULATION OF FUZZY-RANDOM VARIABLES 76 6.
APPLICATIONS IN STRUCTURAL RELIABILITY ANALYSIS 78 7. CONCLUSIONS 79 8.
REFERENCES ...79 CHAPTER 6. FUZZY SIGNAL DETECTION WITH MULTIPLE
WAVEFORM FEATURES 81 /. ROBERT BOSTON, UNIVERSITY OF PITTSBURGH, PA, USA
1. INTRODUCTION 81 2. FUZZY SIGNAL DETECTOR 83 3. METHODS 85 3.1
GENERATION OF SIMULATION DATA 85 3.2 EVALUATION 86 4. RESULTS ON
SIMULATED DATA 87 5. DISCUSSION 92 6. CONCLUSION 94 7. REFERENCES 95
CHAPTER 7. UNCERTAINTY MODELING OF NORMAL VIBRATIONS 9 7 MATTHIAS KUDRA,
UNIVERSITY OF LEIPZIG, GERMANY 1. INTRODUCTION 97 2. NORMAL VIBRATIONS
AND THEIR UNCERTAINTY 98 3. FUZZY OBSERVATION AND PROBABILITY
INTERPRETATION 99 4. FUZZY EVALUATION AND BAYESIAN DECISION 102 4.1
FUZZY EVALUATION 102 4.2 BAYESIAN DECISION 103 5. THE ZEOLITE EXAMPLE
104 6. REFERENCES 107 VLLL CHAPTER 8. MODELING AND IMPLEMENTATION OF
FUZZY TIME POINT REASONING IN MICROPROCESSOR SYSTEMS 109 5. M, YUEN, AND
K. P. LAM, THE CHINESE UNIVERSITY OF HONG KONG, SHATIN, HONG KONG 1.
INTRODUCTION 109 2. PROBLEM DOMAIN 110 2.1 BASICS OF MC68000 READ CYCLE
111 2.2 INTRINSIC PROBLEM OF TIME IMPRECISION 112 3. FUZZY TIME POINT
MODELS 112 3.1 CONCEPT OF FUZZY NUMBERS 113 3.2 DEFINITION OF FUZZY TIME
POINTS 113 3.3 SEMI-BOUNDED FUZZY TIME POINTS 114 4. FUZZY TIME POINT
REASONING 116 4.1 FUZZY TIME POINTS PROPAGATION 117 4.2 FUZZY TIME
POINTS SATISFACTION 118 5. SYSTEM IMPLEMENTATION 120 5.1 VARIATION OF
SEMI-BOUNDED FTPS MEMBERSHIP FUNCTION 120 5.2 VARIATION OF UF,P 121 5.3
VARIATION OF K 123 6. CONCLUSION 124 7. REFERENCES 125 CHAPTER 9. MODEL
LEARNING WITH BAYESIAN NETWORKS FOR TARGET RECOGNITION 127 JUN LIU, AND
KUO-CHU CHANG, GEORGE MASON UNIVERSITY, FAIRFAX, VA, USA 1. INTRODUCTION
127 2. FEATURE-BASED TARGET RECOGNITION WITH A BAYESIAN NETWORK 129 2.1
FEATURE SELECTION 129 2.2 CONSTRUCTION OF A BAYESIAN NETWORK 130 2.3
ESTIMATION OF CONDITIONAL PROBABILITY DISTRIBUTIONS 130 2.4 DECISION
MAKING 131 3. ATR WITH A SIMPLE ASSUMED MODEL 131 4. ATR BY LEARNING
BAYESIAN NETWORKS 136 5. CONCLUSION ; 140 6. REFERENCES 141 CHAPTER 10.
SYSTEM LIFE CYCLE OPTIMIZATION UNDER UNCERTAINTY 143 ODD ANDREAS
ASBJORNSEN, SMART INTERNATIONAL CORPORATION, TRONDHEIM, NORWAY 1.
INTRODUCTION 143 2. SYSTEM LIFE CYCLE MODELS 144 IX 3. PROBABILITY
DENSITY FUNCTIONS FOR THE VARIABLES 146 3.1 THE NORMAL DISTRIBUTION 146
3.2 THE TRIANGULAR DISTRIBUTION 147 4. ANALYSIS OF VARIANCE 150 5.
TRADE-OFF OPTIMIZATION 151 6. THE DECISION VARIABLES 154 7. REFERENCES
156 CHAPTER 11. VALUATION-BASED SYSTEMS FOR PAVEMENT MANAGEMENT DECISION
MAKING 157 NII O. ATTOH-OKINE, FLORIDA INTERNATIONAL UNIVERSITY, MIAMI,
USA 1. INTRODUCTION 157 2. APPROACHES TO DECISION ANALYSIS IN PMS 159 3.
VALUATION-BASED SYSTEMS 161 3.1 BACKGROUND 161 3.2 VARIABLES AND FRAMES
163 3.3 VALUATIONS 163 3.4 NON-ZERO VALUATIONS 163 3.5 PRECEDENCE
CONSTRAINTS 163 3.6 SOLUTION OF VBS 164 4. VALUATION-BASED SYSTEMS IN
PAVEMENT MANAGEMENT SYSTEMS DECISION-MAKING 166 5. CONCLUSION 175 6.
REFERENCES 176 HI. FUZZY-NEURO DATA ANALYSIS AND FORECASTING CHAPTER 12.
HYBRID LEAST-SQUARE REGRESSION ANALYSIS 179 YUN-HSI OSCAR CHANG,
NATIONAL KAOHSIUNG SCIENTIFIC INSTITUTE OF TECHNOLOGY, TAIWAN, ROC;
ANDBILALM. AYYUB, UNIVERSITY OF MARYLAND, USA 1. INTRODUCTION 179 2.
WEIGHTED FUZZY ARITHMETIC * , 180 2.1 WEIGHTED FUZZY ADDITION 180 2.2
WEIGHTED FUZZY SUBTRACTION 181 2.3 WEIGHTED FUZZY MULTIPLICATION 181 2.4
WEIGHTED FUZZY DIVISION 181 3. HYBRID LEAST-SQUARES LINEAR REGRESSION
181 3.1 BIVARIATE REGRESSION MODEL 182 3.2 MULTIPLE REGRESSION MODEL 184
4. NUMERICAL EXAMPLES 185 4.1 BIVARIATE REGRESSION MODEL 186 4.2
MULTIPLE REGRESSION MODEL 187 5. HYBRID LEAST-SQUARES NONLINEAR
REGRESSION 190 6. SUMMARY AND CONCLUSIONS 190 7. REFERENCES 191 CHAPTER
13. LINEAR REGRESSION WITH RANDOM FUZZY NUMBERS 193 WOLFGANG NDTHER, AND
RALF KORNER, FREIBERG UNIVERSITY OF MINING AND TECHNOLOGY, GERMANY 1.
INTRODUCTION 193 2. PRELIMINARIES 194 3. EXTENDED CLASSICAL ESTIMATES
197 4. BEST LINEAR.UNBIASED ESTIMATES 200 5. LEAST SQUARES ESTIMATES 203
5.1 THE CRISP INPUT - FUZZY OUTPUT - CASE 204 5.2 THE FUZZY INPUT -
FUZZY OUTPUT - CASE 207 6. CONCLUDING REMARK 211 7. REFERENCES 211
CHAPTER 14. NEURAL NET SOLUTIONS TO SYSTEMS OF FUZZY LINEAR EQUATIONS
213 JAMES J. BUCKLEY, UNIVERSITY OF ALABAMA AT BIRMINGHAM, USA; THOMAS
FEURING, UNIVERSITY OF MIINSTER, GERMANY; AND YOICHI HAYASHI, MEIJI
UNIVERSITY, KAWASAKI, JAPAN 1. INTRODUCTION 213 2. APPLICATIONS 215 2.1
ENGINEERING 215 2.2 BUSINESS 215 3. NEW SOLUTION 217 4. INTERVAL
ARITHMETIC SOLUTION 218 5. NEURAL NET 219 6. EXAMPLES AND COMPUTER
EXPERIMENTS 222 7. SUMMARY AND CONCLUSIONS 227 8. REFERENCES ....232
CHAPTER 15. FUZZY LOGIC: A CASE STUDY IN PERFORMANCE MEASUREMENT 233
SALWA AMMAR, AND RONALD WRIGHT, LEMOYNE COLLEGE, SYRACUSE, NEW YORK, USA
1. INTRODUCTION 233 XI 2. CASE IN PERFORMANCE MEASUREMENT 234 2.1
ORIGINAL MODEL.. 234 2.2 PRELIMINARY CORRECTION MODEL 236 3. FUZZY
RULE-BASED MODEL 238 4. SYSTEM IMPLEMENTATION 239 5. CONCLUDING REMARKS
243 6. REFERENCES 245 CHAPTER 16. FUZZY GENETIC ALGORITHM BASED APPROACH
TO MACHINE LEARNING UNDER UNCERTAINTY 247 /. BURAK OZYURT LAWRENCE O.
HALL, UNIVERSITY OF SOUTH FLORIDA, TAMPA, USA 1. INTRODUCTION 247 2.
DESCRIPTION OF FGALS 248 2.1 FCM BASED PARTITIONER 248 2.2 FUZZY GENETIC
ALGORITHM BASED LEARNER (FGAL) 250 2.3 RULE EVALUATION 252 3. CASE
STUDY-SYSCHEM PLANT FAULT DIAGNOSIS 254 3.1 DISCUSSION 254 4. CONCLUSION
AND FUTURE DIRECTIONS 257 5. REFERENCES 258 IV. FUZZY-NEURO SYSTEMS
CHAPTER 17. RECURRENT NEURB-FUZZY MODELS OF COMPLEX SYSTEMS 259 CAN
IS.IK, MOHAMMED FARROKHI, JIANN-HORNG LIN, AND A. METE CAKMAKCI,
SYRACUSE UNIVERSITY, NEW YORK, USA 1. INTRODUCTION 259 2. A RECURRENT
NEURO-FUZZY SYSTEM 261 3. APPROXIMATION POWER OF RNF 263 4. RNF MODEL OF
A ROBOT MANIPULATOR 264 5. RNF LEARNING ALGORITHM 265 6. MODULAR RNF .-.
266 7. ANTECEDENTS OF RECURRENT RULES 267 8. SIMULATION RESULTS 268 9.
CONCLUSION 269 10. REFERENCES 270 XU CHAPTER 18. ADAPTIVE FUZZY SYSTEMS
WITH SINUSOIDAL MEMBERSHIP FUNCTIONS 273 LIANG JIN, AND MADAN M. GUPTA,
UNIVERSITY OF SASKATCHEWAN, SASKATOON, CANADA 1. INTRODUCTION 273 2.
MULTILAYERED NEURAL NETWORKS (MNN S) WITH NORMALIZATION 275 3. FUZZY
BASIS FUNCTION NETWORKS (FBFNS) 276 4. SINUSOIDAL MEMBERSHIP FUNCTIONS
278 5. EQUIVALENCE BETWEEN FBFNS AND MNNS 281 6. SUPERVISED PARAMETER
LEARNING ALGORITHMS 283 6.1 PARAMETER TUNING EQUATIONS 283 6.2 THE
MOMENTUM VERSION 284 7. AN EXAMPLE 284 8. CONCLUSIONS 288 9. REFERENCES
288 V. FUZZY DECISION MAKING AND OPTIMIZATION CHAPTER 19. A
COMPUTATIONAL METHOD FOR FUZZY OPTIMIZATION 291 WELDON A. LODWICK, AND
K. DAVID JAMISON, UNIVERSITY OF COLORADO AT DENVER, USA 1. INTRODUCTION
291 2. FUZZY OPTIMIZATION 294 3. THE OPTIMUM OF A SET OF FUZZY NUMBERS -
DEFUZZIFICATION AND OPTIMIZATION . 296 4. A COMPUTATIONAL METHOD FOR
FUZZY OPTIMIZATION PROBLEMS 297 5. NUMERICAL EXAMPLES 297 6. CONCLUSIONS
299 7. REFERENCES 300 CHAPTER 20. INTERACTION OF FUZZY KNOWLEDGE
GRANULES FOR CONJUNCTIVE LOGIC 301 THOMAS WHALEN, GEORGIA STATE
UNIVERSITY, ATLANTA, USA 1. INTRODUCTION 301 2. MEMBERSHIP FUNCTION OF
FUZZY OUTPUT 302 3. DEFUZZIFIED OUTPUT VALUE Y* 303 3.1 DERIVATION OF
THE NUMERATOR 304 3.2 DERIVATION OF THE DENOMINATOR 307 3.3 EXACT
SOLUTION FOR Y* 308 3.4 FUZZY CONTINUITY 308 XU1 4. NUMERICAL EXAMPLE
309 5. DISCUSSION 311 6. REFERENCES 311 CHAPTER 21. FUZZY DECISION
PROCESSES WITH EXPECTED FUZZY REWARDS 313 YUJI YOSHIDA, KITAKYUSHU
UNIVERSITY, JAPAN 1. INTRODUCTION 313 2. FUZZY DECISION PROCESSES WITH A
DISCOUNTED TOTAL REWARD 314 3. FUZZY EXPECTATION AND OPTIMAL FUZZY
POLICIES 317 4. A NUMERICAL EXAMPLE 320 4.1 SCHWEIZER AND SKLAR CLASS T
SS 321 4.2 YAGER CLASS T SY 322 5. CONCLUSIONS 322 6. ACKNOWLEDGMENTS
322 7. REFERENCES 322 CHAPTER 22. ON THE COMPUTABILITY OF POSSIBILISTIC
RELIABILITY 325 BART CAPPELLE, AND ETIENNE E. KERRE, UNIVERSITY OF GENT,
BELGIUM 1. INTRODUCTION 325 2. STRUCTURE FUNCTIONS 327 3. POSSIBILISTIC
RELIABILITY FUNCTIONS 329 4. OBSERVATIONS 330 5. THE COMPUTATION OF
POSSIBILISTIC RELIABILITY 333 6. AN APPLICATION TO DUAL NECESSITY
MEASURES 334 7. CONCLUSION 336 8. REFERENCES 336 CHAPTER 23. DISTRIBUTED
REASONING WITH UNCERTAIN DATA 339 KERSTIN SCHILL, UNIVERSITY OF MUNICH,
GERMANY 1. INTRODUCTION 339 2. THE ANALYSIS OF UNCERTAIN DATA 340 3.
DISTRIBUTED REASONING BY INFORMATION GAIN 342 4. DISTRIBUTED REASONING
IN A KNOWLEDGE BASE WITH MULTIPLE HIERARCHIES 347 5. INFORMATION GAIN AS
A CRITERION FOR SEARCH 348 6. CONCLUSION 349 7. REFERENCES 350 XIV
CHAPTER 24. A FRESH PERSPECTIVE ON UNCERTAINTY MODELING: UNCERTAINTY VS.
UNCERTAINTY MODELING 35 3 HANS-JURGEN ZIMMERMANN, RWTH AACHEN, GERMANY
1. INTRODUCTION 353 2. CAUSES OF UNCERTAINTY 356 3. TYPE OF AVAILABLE
INFORMATION 358 4. TYPE OF INFORMATION PROCESSING 360 5. TYPE OF
REQUIRED INFORMATION 360 6. UNCERTAINTY THEORIES 361 7. CONCLUSIONS 362
8. REFERENCES 363 SUBJECT INDEX 365 ABOUT THE EDITORS 371
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| id | DE-604.BV012040201 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T18:20:35Z |
| institution | BVB |
| isbn | 0792380304 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-008147840 |
| oclc_num | 246973138 |
| open_access_boolean | |
| owner | DE-703 DE-83 |
| owner_facet | DE-703 DE-83 |
| physical | XXIV, 370 S. graph. Darst. |
| publishDate | 1997 |
| publishDateSearch | 1998 |
| publishDateSort | 1998 |
| publisher | Kluwer |
| record_format | marc |
| series | International series in intelligent technologies |
| series2 | International series in intelligent technologies |
| spelling | Uncertainty analysis in engineering and sciences fuzzy logic, statistics, and neural network approach Bilal M. Ayyub ; Madan M. Gupta Boston [u.a.] Kluwer 1997 [erschienen] 1998 XXIV, 370 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier International series in intelligent technologies 11 Ingenieurwissenschaften Mathematisches Modell Engineering Statistical methods Fuzzy logic Neural networks (Computer science) Reliability (Engineering) Uncertainty Mathematical models Fuzzy-Logik (DE-588)4341284-1 gnd rswk-swf Fuzzy-Menge (DE-588)4061868-7 gnd rswk-swf Ingenieurwissenschaften (DE-588)4137304-2 gnd rswk-swf Ingenieurwissenschaften (DE-588)4137304-2 s Fuzzy-Menge (DE-588)4061868-7 s Fuzzy-Logik (DE-588)4341284-1 s DE-604 Ayyub, Bilal M. Sonstige oth Gupta, Madan M. Sonstige oth International series in intelligent technologies 11 (DE-604)BV010552630 11 GBV Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=008147840&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Uncertainty analysis in engineering and sciences fuzzy logic, statistics, and neural network approach International series in intelligent technologies Ingenieurwissenschaften Mathematisches Modell Engineering Statistical methods Fuzzy logic Neural networks (Computer science) Reliability (Engineering) Uncertainty Mathematical models Fuzzy-Logik (DE-588)4341284-1 gnd Fuzzy-Menge (DE-588)4061868-7 gnd Ingenieurwissenschaften (DE-588)4137304-2 gnd |
| subject_GND | (DE-588)4341284-1 (DE-588)4061868-7 (DE-588)4137304-2 |
| title | Uncertainty analysis in engineering and sciences fuzzy logic, statistics, and neural network approach |
| title_auth | Uncertainty analysis in engineering and sciences fuzzy logic, statistics, and neural network approach |
| title_exact_search | Uncertainty analysis in engineering and sciences fuzzy logic, statistics, and neural network approach |
| title_full | Uncertainty analysis in engineering and sciences fuzzy logic, statistics, and neural network approach Bilal M. Ayyub ; Madan M. Gupta |
| title_fullStr | Uncertainty analysis in engineering and sciences fuzzy logic, statistics, and neural network approach Bilal M. Ayyub ; Madan M. Gupta |
| title_full_unstemmed | Uncertainty analysis in engineering and sciences fuzzy logic, statistics, and neural network approach Bilal M. Ayyub ; Madan M. Gupta |
| title_short | Uncertainty analysis in engineering and sciences |
| title_sort | uncertainty analysis in engineering and sciences fuzzy logic statistics and neural network approach |
| title_sub | fuzzy logic, statistics, and neural network approach |
| topic | Ingenieurwissenschaften Mathematisches Modell Engineering Statistical methods Fuzzy logic Neural networks (Computer science) Reliability (Engineering) Uncertainty Mathematical models Fuzzy-Logik (DE-588)4341284-1 gnd Fuzzy-Menge (DE-588)4061868-7 gnd Ingenieurwissenschaften (DE-588)4137304-2 gnd |
| topic_facet | Ingenieurwissenschaften Mathematisches Modell Engineering Statistical methods Fuzzy logic Neural networks (Computer science) Reliability (Engineering) Uncertainty Mathematical models Fuzzy-Logik Fuzzy-Menge |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=008147840&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV010552630 |
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