Evolving rule based models: a tool for design of flexible adaptive systems ; with 9 tables
Gespeichert in:
| Beteilige Person: | |
|---|---|
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
Heidelberg [u.a.]
Physica-Verl.
2002
|
| Schriftenreihe: | Studies in fuzziness and soft computing
92 |
| Schlagwörter: | |
| Links: | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017475917&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| Umfang: | XIII, 213 S. graph. Darst. |
| ISBN: | 3790814571 |
Internformat
MARC
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| 100 | 1 | |a Angelov, Plamen P. |d 1966- |e Verfasser |0 (DE-588)123411963 |4 aut | |
| 245 | 1 | 0 | |a Evolving rule based models |b a tool for design of flexible adaptive systems ; with 9 tables |c Plamen P. Angelov |
| 264 | 1 | |a Heidelberg [u.a.] |b Physica-Verl. |c 2002 | |
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| 490 | 1 | |a Studies in fuzziness and soft computing |v 92 | |
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Datensatz im Suchindex
| _version_ | 1819352854097297408 |
|---|---|
| adam_text | PLAMEN P. ANGELOV EVOLVING RULE-BASED MODELS A TOOL FOR DESIGN OF
FLEXIBLE ADAPTIVE SYSTEMS WITH 106 FIGURES AND 9 TABLES: PHYSICA-VERLAG
A SPRINGER-VERLAG COMPANY CONTENTS PREFACE VII 1 INTRODUCTION 1 1.1
FLEXIBLE MODELS - AN OPPORTUNITY FOR CONTROL THEORY . 1 1.2 FLEXIBLE
MODELS AND THEIR IDENTIFICATION 2 1.2.1 EXPERT KNOWLEDGE AND PARAMETERS
TUNING . . . 3 1.2.2 DATA-DRIVEN TECHNIQUES 4 1.2.3 PRECISION AND
TRANSPARENCY 5 1.2.4 THE NEED FOR ON-LINE ALGORITHMS 6 1.3 INTELLIGENT
ADAPTIVE SYSTEMS - A HIGHER LEVEL OF CONTROL 7 1.4 STRUCTURE OF THE BOOK
7 PART I SYSTEM MODELLING: BASIC PRINCIPLES 11 2 CONVENTIONAL MODELS 13
2.1 FIRST PRINCIPLES MODELS 13 2.1.1 HEATING/COOLING COIL MODEL 14 2.1.2
FERMENTATION PROCESS MODEL 15 2.2 BLACK-BOX MODELS 17 2.2.1 LINEAR
BLACK-BOX MODELS 17 2.2.2 POLYNOMIAL MODELS 18 2.2.3 REGRESSION MODELS
19 / 2.2.4 NEURAL NETWORKS 20 2.2.4.1 RADIAL-BASIS FUNCTIONS (RBF)
NEURAL NETWORKS . 21 2.2.4.2 HYBRID NN-FIRST PRINCIPLES MODEL OF A . . .
FERMENTATION PROCESS 22 2.3 CONCLUSION 22 3 FLEXIBLE MODELS 25 3.1 FUZZY
SET THEORY: BASIC INTRODUCTION 26 CONTENTS 3.1.1 FUZZY SET DEFINITION 27
3.1.2 BASIC OPERATIONS OVER FUZZY SETS 28 3.1.2.1 T-NORMS 28 3.1.2.2
S-NORMS 29 3.1.2-^NEGATION 29 3.1.2.4DE-FUZZIFICATION 30 3.1.2.5 DEGREE
OF SIMILARITY BETWEEN FUZZY SETS . . . 31 3.2 MODELS WITH FLEXIBLE
PARAMETERS OR (INEQUALITIES . . . 31 3.2.1 MODELS WITH FLEXIBLE
PARAMETERS 31 3.2.2 MODELS WITH FLEXIBLE (IN)EQUALITIES 32 3.3 FLEXIBLE
RULE-BASED MODELS 34 3.3.1 FLEXIBLE RELATIONAL MODELS 35 3.3.2 MAMDANI
TYPE MODELS 36 3.3.3 TAKAGI-SUGENO TYPE MODELS 37 3.4 CONCLUSION 41 PART
II FLEXIBLE MODEL S IDENTIFICATION . . 43 NON-LINEAR APPROACH TO
(OFF-LINE) IDENTIFICATION OF FLEXIBLE MODELS 47 4.1 IDENTIFICATION
PROBLEM FORMULATION 47 4.1.1 IDENTIFICATION CRITERIA . 48 4.2 GA -BRIEF
INTRODUCTION 49 4.3 CENTRE-OF-GRAVITY-BASED CROSSOVER OPERATOR 52 4.3.1
COG-BASED CROSS-OVER OPERATOR - HOW IT WORKS . . . 54 4.3.2 COG-BASED
OPERATOR - WHY IT WORKS 54 4.3.3 TEST EXAMPLES 57 4.4 ENCODING AND
DECODING INDICES OF FLEXIBLE RULES . . . . AND LINGUISTIC TERMS 57 4.4.1
ENCODING PROCEDURE 59 4.4.2 DECODING A FLEXIBLE RULE 59 ,4.5 ALGORITHM
OF THE NON-LINEAR APPROACH 62 4.6 CONCLUSION 63 QUASI-LINEAR APPROACH TO
FRB MODELS (OFF-LINE) IDENTIFICATION . . 67 5.1 DATA SPACE CLUSTERING 67
5.2 SUBTRACTIVE CLUSTERING 71 5.3 PARAMETERS (OF THE CONSEQUENT PART)
ESTIMATION 72 5.4 FLEXIBLE RULE-BASED MODEL REFINEMENTS 73 CONTENTS XI
5.4.1 MODEL STRUCTURE SIMPLIFICATION , . . 73 5.4.2 MODEL PARAMETER S
REFINEMENT/OPTIMISATION . . . . 74 5.5 ALGORITHM FOR (OFF-LINE)
QUASI-LINEAR IDENTIFICATION OF FRB . MODELS 75 5.6CONCLUSION . 75 6
INTELLIGENT AND SMART ADAPTIVE SYSTEMS 79 6.1 INTELLIGENT SYSTEMS 79
6.1.1 LOOSE DEFINITION 79 6.1.2 PROBLEMS 80 6.1.3 IMPORTANCE 80 6.1.4
SPECIFICS 81 6.2 SMART ADAPTIVE SYSTEMS 81 6.2.1 THE ISSUE OF SMART
ADAPTIVE SYSTEMS 81 6.2.2 FEATURES OF A SMART ADAPTIVE SYSTEM 82 6.2.3
PRACTICAL IMPLICATIONS 82 6.2.4 INTELLIGENT INDOOR CLIMATE,CONTROL
SYSTEM 83 6.3 CONCLUSION 84 7 ON-LINE IDENTIFICATION OF FZEJCIWE
TSK-TYPE MODELS . . . . 87 7.1 THE CONCEPT 87 7.2 BASIC PHASES OF THE
PROCEDURE 88 7.3 POTENTIALS UP-DATE IN ON-LINE MODE 89 7.4 RULE-BASE
INNOVATION AND MODIFICATION MECHANISM . . . . 92 7.5 PARAMETERS UP-DATE
96 7.6FRB MODEL UP-GRADE; LEARNING TROUGH EXPERIENCE . . . 99 7.7
RULE STRUCTURE AND PARAMETERS TUNING AND REFINEMENT . . . 99 7.7.1
SIMILARITY-BASED SIMPLIFICATION OF LINGUISTIC TERMS . . 100 7.7.2
PARAMETERS REFINEMENT (TUNING) BY NON-LINEAR . . OPTIMISATION 101 7.8
FLOW-CHART OF THE ALGORITHM 102 7.9 ER CONTROL ALGORITHM 104 7.10
CONCLUSION 108 PART III ENGINEERING APPLICATIONS ILL 8 MODELLING INDOOR
CLIMATE CONTROL SYSTEMS 115 8.1 MODELLING COMPONENTS OF HVAC SYSTEMS 116
XII CONTENTS 8.1.1 HEATING/COOLING COIL MODELLING 117 8.1.1.1 MODELLING
OUTLET (FROM THE COIL) AIR TEMPERATURE 119 8.1.1.2 MODELLING HEAT
TRANSFER IN A HEATING/COOLING COIL 123 8.1.2 DUCTED FAN MODELLING 124
8.1.3 MODELLING EFFICIENCY OF BOILERS: HYBRID MODEL APPROACH 130 8.2
MODELLING THE THERMAL LOAD OF A BUILDING 131 8.3 LEARNING TROUGH
EXPERIENCE (VL STRATEGY) . . . . . . 138 8.4 ON-LINE MODELLING DYNAMICAL
SIGNALS 142 8.5 MODEL SIMPLIFICATION BY LINGUISTIC TERM S REDUCTION . .
147 8.6 REFINEMENT OF LINGUISTIC TERMS PARAMETERS 151 8.7 TESTING THE
NEW COG-BASED CROSSOVER OPERATOR . . . . 154 8.7.1 NUMERICAL TEST
FUNCTIONS (NF1-NF5) 154 8.7.1.1 DEJONG S FUNCTION (NF1) 155 8.7.1.2
RASTRIGIN S FUNCTION (NF2) 156 8.7.1.3 SUM OF DIFFERENT POWERS (NF3) 157
8.7.1.4 SCHWEFEL S FUNCTION (NF4) 158 8.7.1.5 GRIEWANGK S FUNCTION (NF5)
159 8.7.2 OPTIMAL SCHEDULING OF A HOLLOW CORE VENTILATED . . SLABS (AC)
160 8.8 ICC SYSTEM - OPEN OR CLOSED LOOP? A SYSTEM APPROACH . 164 8.9
CONCLUSION 166 9 ON-LINE MODELLING OF FERMENTATION PROCESSES 169 9.1
BIO-PROCESSES - SPECIFICS OF THEIR MODELLING 169 9.2 ER MODEL OF A
FERMENTATION PROCESS 171 9.2.1 LACTOSE OXIDATION - PROCESS SPECIFICS 171
9.2.2 EXPERIMENTAL DATA 172 9.2.3 MODELLING THE PROCESS 173 9.2.3.1
FIRST PRINCIPLES-BASED MODEL 173 9.2.3.2ER MODEL 174 9.2.3.3 ANALYSIS OF
THE RESULTS 178 9.3 CONCLUSION 178 10 INTELLIGENT RISK ASSESSMENT 181
10.1 APPLICATION OF ER MODELS IN CREDIT WORTHINESS ASSESSMENT . 181
10.1.1 CREDITWORTHINESS ASSESSMENT: PROBLEM SPECIFICS . . . 182 10.1.2
FLEXIBLE RULE-BASED SYSTEM 182 10.1.3 CREDIT RISK ASSESSMENT BY A
FLEXIBLE RULE-BASED SYSTEM 182 10.2 INTELLIGENT EVOLVING SYSTEM FOR RISK
ASSESSMENT IN CIVIL. AVIATION 187 CONTENTS XIII 10.2.1 SPECIFICS AND
IMPORTANCE OF THE PROBLEM . . . . 187 10.2.2 INTELLIGENT TECHNOLOGIES
FOR RISK ASSESSMENT . . IN CIVIL AVIATION 188 10.3 /NTEND: EVOLVING
DISTRIBUTED INTELLIGENCE SYSTEM . . FOR EVALUATION OF TENDERING 190 10.4
CONCLUSION 191 11 CONCLUSIONS 193 REFERENCES 199 INDEX 209
|
| any_adam_object | 1 |
| author | Angelov, Plamen P. 1966- |
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| author_facet | Angelov, Plamen P. 1966- |
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| author_sort | Angelov, Plamen P. 1966- |
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| building | Verbundindex |
| bvnumber | BV023833773 |
| ctrlnum | (OCoLC)248629030 (DE-599)BVBBV023833773 |
| format | Book |
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| id | DE-604.BV023833773 |
| illustrated | Illustrated |
| indexdate | 2024-12-20T13:35:02Z |
| institution | BVB |
| isbn | 3790814571 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-017475917 |
| oclc_num | 248629030 |
| open_access_boolean | |
| owner | DE-634 DE-525 |
| owner_facet | DE-634 DE-525 |
| physical | XIII, 213 S. graph. Darst. |
| publishDate | 2002 |
| publishDateSearch | 2002 |
| publishDateSort | 2002 |
| publisher | Physica-Verl. |
| record_format | marc |
| series | Studies in fuzziness and soft computing |
| series2 | Studies in fuzziness and soft computing |
| spellingShingle | Angelov, Plamen P. 1966- Evolving rule based models a tool for design of flexible adaptive systems ; with 9 tables Studies in fuzziness and soft computing Künstliche Intelligenz (DE-588)4033447-8 gnd Fuzzy-Logik (DE-588)4341284-1 gnd |
| subject_GND | (DE-588)4033447-8 (DE-588)4341284-1 |
| title | Evolving rule based models a tool for design of flexible adaptive systems ; with 9 tables |
| title_auth | Evolving rule based models a tool for design of flexible adaptive systems ; with 9 tables |
| title_exact_search | Evolving rule based models a tool for design of flexible adaptive systems ; with 9 tables |
| title_full | Evolving rule based models a tool for design of flexible adaptive systems ; with 9 tables Plamen P. Angelov |
| title_fullStr | Evolving rule based models a tool for design of flexible adaptive systems ; with 9 tables Plamen P. Angelov |
| title_full_unstemmed | Evolving rule based models a tool for design of flexible adaptive systems ; with 9 tables Plamen P. Angelov |
| title_short | Evolving rule based models |
| title_sort | evolving rule based models a tool for design of flexible adaptive systems with 9 tables |
| title_sub | a tool for design of flexible adaptive systems ; with 9 tables |
| topic | Künstliche Intelligenz (DE-588)4033447-8 gnd Fuzzy-Logik (DE-588)4341284-1 gnd |
| topic_facet | Künstliche Intelligenz Fuzzy-Logik |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017475917&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV023816344 |
| work_keys_str_mv | AT angelovplamenp evolvingrulebasedmodelsatoolfordesignofflexibleadaptivesystemswith9tables |