Hybrid self-organizing modeling systems:
Gespeichert in:
| Weitere Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Berlin ; Heidelberg
Springer
2009
|
| Schriftenreihe: | Studies in computational intelligence
211 |
| Schlagworte: | |
| Online-Zugang: | BTU01 FHN01 FHR01 Volltext Inhaltsverzeichnis |
| Beschreibung: | Literaturangaben |
| Beschreibung: | 1 Online-Ressource (XVII, 280 S.) Ill., graph. Darst. |
| ISBN: | 9783642015298 9783642015304 |
| DOI: | 10.1007/978-3-642-01530-4 |
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Datensatz im Suchindex
| _version_ | 1804139355679227904 |
|---|---|
| adam_text | IMAGE 1
CONTENTS
HYBRID COMPUTATIONAL INTELLIGENCE AND GMDH SYSTEMS 1 GODFREY ONWUBOLU 1
INTRODUCTION 1
2 GROUP METHOD OF DATA HANDLING (GMDH) NETWORKS 2 2.1 GMDH LAYERS 3
2.2 GMDH NODES 3
2.3 GMDH CONNECTIONS 4
2.4 GMDH NETWORK 5
2.5 REGULARIZED MODEL SELECTION 5
2.6 GMDH ALGORITHM 6
2.7 ADVANTAGES OF GMDH TECHNIQUE 7
2.8 LIMITATIONS OF GMDH TECHNIQUE 7
3 RATIONALE FOR HYBRID SYSTEMS 11
4 COMPUTATIONAL INTELLIGENCE 12
4.1 WHAT IS INTELLIGENCE? 13
4.2 CAN COMPUTERS BE INTELLIGENT? 13
4.3 COMPUTATIONAL INTELLIGENCE PARADIGMS 14
5 HYBRID GMDH SYSTEMS 21
6 CONCLUSION 23
REFERENCES 23
HYBRID GENETIC PROGRAMMING AND GMDH SYSTEM: STROGANOFF 27
IBA HITOSHI 1 INTRODUCTION 27
2 BACKGROUND 28
2.1 SYSTEM IDENTIFICATION PROBLEMS 28
2.2 DIFFICULTIES WITH TRADITIONAL GP 29
2.3 NUMERICAL APPROACH TO GP 31
3 PRINCIPLES OF STROGANOFF 31
BIBLIOGRAFISCHE INFORMATIONEN HTTP://D-NB.INFO/993396135
DIGITALISIERT DURCH
IMAGE 2
XIV CONTENTS
3.1 STROGANOFF ALGORITHM 31
3.2 GMDH PROCESS IN STROGANOFF 32
3.3 CROSSOVER IN STROGANOFF 34
3.4 MUTATION IN STROGANOFF 35
3.5 FITNESS EVALUATION IN STROGANOFF 36
3.6 OVERALL FLOW OF STROGANOFF 36
3.7 RECOMBINATION GUIDANCE IN STROGANOFF 38
4 NUMERICAL PROBLEMS WITH STROGANOFF 40
4.1 TIME SERIES PREDICTION WITH STROGANOFF 40
4.2 COMPARISON WITH A TRADITIONAL GP 43
4.3 STATISTICAL COMPARISON OF STROGANOFF AND A TRADITIONAL GP 46
5 SYMBOLIC PROBLEMS WITH STROGANOFF 52
5.1 EXTENSION OF STROGANOFF 52
5.2 SYMBOLIC REGRESSION 53
6 APPLYING STROGANOFF TO COMPUTATIONAL FINANCES 59 6.1 PREDICTING STOCK
MARKET DATA 60
6.2 DEVELOPPING DAY-TRADING RULES 68
7 INDUCTIVE GENETIC PROGRAMMING 72
7.1 POLYNOMIAL NEURAL NETWORKS 73
7.2 PNN APPROACHES 74
7.3 BASIC IGP FRAMEWORK 77
7.4 PNN VS. LINEAR ARMA MODELS 79
7.5 PNN VS. NEURAL NETWORK MODELS 80
8 DISCUSSION 83
8.1 COMPARISON OF STROGANOFF AND TRADITIONAL GP 83 8.2 GENETIC
PROGRAMMING WITH LOCAL HILL CLIMBING . .. 84 8.3 LIMITATIONS AND FURTHER
EXTENSIONS OF STROGANOFF 89
8.4 APPLICABILITY TO COMPUTATIONAL FINANCES 90
9 CONCLUSION 91
HYBRID GENETIC ALGORITHM AND G M DH SYSTEM 99
NADER NARIMAN-ZADEH AND JAMALI ALI 1 INTRODUCTION 99
2 MODELLING USING GMDH-TYPE NEURAL NETWORKS 101
3 HYBRID GENETIC/SVD DESIGN OF GMDH-TYPE NEURAL NETWORKS 103
3.1 APPLICATION OF SVD IN THE DESIGN OF GMDH-TYPE NETWORKS 103
3.2 APPLICATION OF SVD IN THE DESIGN OF GMDH-TYPE NETWORKS 103
4 SINGLE-OBJECTIVE HYBRID GENETIC DESIGN OF GMDH-TYPE NEURAL NETWORKS
MODELLING AND PREDICTION OF COMPLEX PROCESSES 108
IMAGE 3
CONTENTS XV
4.1 APPLICATION TO THE MODELLING AND PREDICTION OF LEVEL VARIATIONS OF
THE CASPIAN SEA 108
4.2 APPLICATION TO THE MODELLING AND PREDICTION OF THE EXPLOSIVE CUTTING
PROCESS I LL
5 MULTI-OBJECTIVE HYBRID GENETIC DESIGN OF GMDH-TYPE NEURAL NETWORKS
MODELLING AND PREDICTION OF COMPLEX PROCESSES 115
5.1 MULTI-OBJECTIVE OPTIMIZATION 116
5.2 MULTI-OBJECTIVE UNIFORM-DIVERSITY GENETIC ALGORITHM (MUGA) 117
5.3 MULTI-OBJECTIVE GENETIC DESIGN OF GMDH-TYPE NEURAL NETWORKS FOR A
VARIABLE VALVE-TIMING SPARK-IGNITION ENGINE 120
5.4 MULTI-OBJECTIVE GENETIC DESIGN OF GMDH-TYPE NEURAL NETWORKS FOR A
NONLINEAR SYSTEM 125
5.5 MULTI-OBJECTIVE GENETIC DESIGN OF GMDH-TYPE NEURAL NETWORKS FOR
MODELLING AND PREDICTION OF EXPLOSIVE CUTTING PROCESS 130
6 CONCLUSION 136
REFERENCES 137
HYBRID DIFFERENTIAL EVOLUTION AND GMDH SYSTEMS 139
GODFREY ONWUBOLU 1 INTRODUCTION 139
2 INDUCTIVE MODELING: GROUP METHOD OF DATA HANDLING (GMDH) 140
2.1 GMDH LAYERS 140
2.2 GMDH NODES 140
2.3 GMDH CONNECTIONS 142
2.4 GMDH NETWORK 142
2.5 ADVANTAGES OF GMDH TECHNIQUE 143
2.6 LIMITATIONS OF GMDH TECHNIQUE 143
3 CLASSICAL DIFFERENTIAL EVOLUTION ALGORITHM 144
3.1 THE STEPS INVOLVED IN CLASSICAL DIFFERENTIAL EVOLUTION 145
3.2 TEN DIFFERENT WORKING STRATEGIES IN DIFFERENTIAL EVOLUTION 147
3.3 DISCRETE DIFFERENTIAL EVOLUTION 148
3.4 PERMUTATIVE POPULATION 149
3.5 FORWARD TRANSFORMATION 149
3.6 BACKWARD TRANSFORMATION 150
3.7 RECURSIVE MUTATION 150
3.8 DISCRETE DIFFERENTIAL EVOLUTION (DDE) 151
3.9 ENHANCED DIFFERENTIAL EVOLUTION (EDE) 152
4 THE HYBRID DIFFERENTIAL EVOLUTION AND GMDH SYSTEM 152
IMAGE 4
XVI CONTENTS
4.1 STRUCTURAL OPTIMIZATION: REPRESENTATION OF ENCODING STRATEGY OF EACH
PARTIAL DESCRIPTOR (PD) 154 4.2 PARAMETRIC OPTIMIZATION: COEFFICIENT
ESTIMATION OF THE POLYNOMIAL CORRESPONDING TO THE SELECTED
NODE (PN) 158
4.3 FRAMEWORK OF THE DESIGN PROCEDURE OF THE DE-GMDH HYBRID SYSTEM 163
4.4 THE HYBRID DE-GMDH ALGORITHM 165
5 DE-GMDH MECHANICS ILLUSTRATED 168
6 APPLICATIONS OF THE DE-GMDH HYBRID SYSTEM 177
6.1 DE-GMDH FOR MODELING THE TOOL-WEAR PROBLEM 177
6.2 EXCHANGE RATES FORECASTING USING THE DE-GMDH PARADIGMS 181
6.3 GAS FURNACE EXPERIMENTATION USING THE DE-GMDH LEARNING NETWORK 186
6.4 CPU TIME COST OF THE DE-GMDH ALGORITHM 187 7 CONCLUSIONS 188
REFERENCES 189
HYBRID PARTICLE SWARM OPTIMIZATION AND GMDH SYSTEM . .. 193 ANURAG
SHARMA AND GODFREY ONWUBOLU 1 INTRODUCTION 193
2 THE GROUP METHOD OF DATA HANDLING (GMDH) 194
2.1 OVERVIEW OF TRADITIONAL GMDH 194
2.2 DRAWBACKS OF TRADITIONAL GMDH 197
3 PARTICLE SWARM OPTIMIZATION ALGORITHM 198
3.1 EXPLOSION CONTROL 200
3.2 PARTICLE SWARM OPTIMIZATION OPERATORS 200
3.3 PARTICLE SWARM OPTIMIZATION NEIGHBORHOOD 202 4 THE PROPOSED HYBRID
PSO-GMDH ALGORITHM 206
4.1 OVERVIEW 206
4.2 TECHNICAL VIEW 209
5 EXPERIMENTATION 221
5.1 TOOL WEAR PROBLEM 221
5.2 GAS FURNACE PROBLEM 226
6 CONCLUSION 229
REFERENCES 230
GAME - HYBRID SELF-ORGANIZING MODELING SYSTEM BASED ON GMDH 233
PAVEL KORDIK 1 INTRODUCTION 233
1.1 SELF-ORGANIZING MODELLING 234
2 GROUP OF ADAPTIVE MODELS EVOLUTION (GAME) 237
2.1 THE CONCEPT OF THE ALGORITHM 237
IMAGE 5
CONTENTS XVII
2.2 CONTRIBUTIONS OF THE GAME ALGORITHM 241
2.3 OPTIMIZATION OF GAME NEURONS 245
2.4 OPTIMIZATION METHODS (SETTING UP COEFFICIENTS) 249
2.5 COMBINING OPTIMIZATION METHODS 255
2.6 STRUCTURAL INNOVATIONS 258
2.7 GENETIC ALGORITHM 259
2.8 ENSEMBLE TECHNIQUES IN GAME 266
3 BENCHMARKING THE GAME METHOD 269
3.1 SUMMARY OF RESULTS 271
4 CASE STUDIES - DATA MINING USING GAME 271
4.1 FETAL WEIGHT PREDICTION FORMULAE EXTRACTED FROM GAME 271
5 THE FAKE GAME PROJECT 273
5.1 THE GOAL OF THE FAKE GAME ENVIRONMENT 275
REFERENCES 278
AUTHOR INDEX 281
|
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| discipline | Informatik Mathematik |
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| id | DE-604.BV035663706 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T21:42:46Z |
| institution | BVB |
| isbn | 9783642015298 9783642015304 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-017718092 |
| oclc_num | 1074294694 |
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| physical | 1 Online-Ressource (XVII, 280 S.) Ill., graph. Darst. |
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| publishDate | 2009 |
| publishDateSearch | 2009 |
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| publisher | Springer |
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| series | Studies in computational intelligence |
| series2 | Studies in computational intelligence |
| spelling | Hybrid self-organizing modeling systems Godfrey C. Onwubolu (ed.) Berlin ; Heidelberg Springer 2009 1 Online-Ressource (XVII, 280 S.) Ill., graph. Darst. txt rdacontent c rdamedia cr rdacarrier Studies in computational intelligence 211 Literaturangaben Komplexes System (DE-588)4114261-5 gnd rswk-swf Selbst organisierendes System (DE-588)4054424-2 gnd rswk-swf Soft Computing (DE-588)4455833-8 gnd rswk-swf Hybrides System (DE-588)4510314-8 gnd rswk-swf Data Mining (DE-588)4428654-5 gnd rswk-swf Modellierung (DE-588)4170297-9 gnd rswk-swf Selbst organisierendes System (DE-588)4054424-2 s Hybrides System (DE-588)4510314-8 s Soft Computing (DE-588)4455833-8 s Komplexes System (DE-588)4114261-5 s Modellierung (DE-588)4170297-9 s Data Mining (DE-588)4428654-5 s DE-604 Onwubolu, Godfrey C. edt Studies in computational intelligence 211 (DE-604)BV020822171 211 https://doi.org/10.1007/978-3-642-01530-4 Verlag Volltext DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017718092&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Hybrid self-organizing modeling systems Studies in computational intelligence Komplexes System (DE-588)4114261-5 gnd Selbst organisierendes System (DE-588)4054424-2 gnd Soft Computing (DE-588)4455833-8 gnd Hybrides System (DE-588)4510314-8 gnd Data Mining (DE-588)4428654-5 gnd Modellierung (DE-588)4170297-9 gnd |
| subject_GND | (DE-588)4114261-5 (DE-588)4054424-2 (DE-588)4455833-8 (DE-588)4510314-8 (DE-588)4428654-5 (DE-588)4170297-9 |
| title | Hybrid self-organizing modeling systems |
| title_auth | Hybrid self-organizing modeling systems |
| title_exact_search | Hybrid self-organizing modeling systems |
| title_full | Hybrid self-organizing modeling systems Godfrey C. Onwubolu (ed.) |
| title_fullStr | Hybrid self-organizing modeling systems Godfrey C. Onwubolu (ed.) |
| title_full_unstemmed | Hybrid self-organizing modeling systems Godfrey C. Onwubolu (ed.) |
| title_short | Hybrid self-organizing modeling systems |
| title_sort | hybrid self organizing modeling systems |
| topic | Komplexes System (DE-588)4114261-5 gnd Selbst organisierendes System (DE-588)4054424-2 gnd Soft Computing (DE-588)4455833-8 gnd Hybrides System (DE-588)4510314-8 gnd Data Mining (DE-588)4428654-5 gnd Modellierung (DE-588)4170297-9 gnd |
| topic_facet | Komplexes System Selbst organisierendes System Soft Computing Hybrides System Data Mining Modellierung |
| url | https://doi.org/10.1007/978-3-642-01530-4 http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017718092&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV020822171 |
| work_keys_str_mv | AT onwubolugodfreyc hybridselforganizingmodelingsystems |