Neural networks for control and systems:
Gespeichert in:
| Format: | Buch |
|---|---|
| Sprache: | English |
| Veröffentlicht: |
London
Peregrinus
1992
|
| Schriftenreihe: | IEE control engineering series
46 |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XIII, 260 S. graph. Darst. |
| ISBN: | 0863412793 |
Internformat
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| 490 | 1 | |a IEE control engineering series |v 46 | |
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Datensatz im Suchindex
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| adam_text | NEURAL NETWORKS FOR CONTROL AND SYSTEMS EDITED BY K. WARWICK, G. W.
IRWIN AND K. J. HUNT PETER PEREGRINUS LTD. ON BEHALF OF THE INSTITUTION
OF ELECTRICAL ENGINEERS CONTENTS LIST OF CONTRIBUTORS PREFACE 1. NEURAL
NETWORKS: AN INTRODUCTION 1 K. WARWICK 1.1 INTRODUCTION 1 1.2 NEURAL
NETWORK PRINCIPLES 2 1.3 NEURAL NETWORK ELEMENTS 3 1.4 HOPFIELD NETWORKS
4 1.5 KOHONEN NETWORKS 5 1.6 BACK PROPAGATION NETWORKS 6 1.7 N-TUPLE
NETWORKS 9 1.8 CONCLUSIONS 11 2. DIGITAL NEURAL NETWORKS 13 A. REDGERS
AND I. ALEKSANDER 2.1 CLASSIFICATION OF ARTIFLCIAL NEURAL NETWORKS 13
2.2 FUNCTIONS OF BOOLEAN NODES 17 2.3 LARGE LOGIC NEURONS 20 2.4
IMPLEMENTING LARGE ANN S USING MAGNUS 26 3. A SURVEY OF NEURAL NETWORKS
FOR CONTROL 31 R. ZBIKOWSKI AND P.J. GAWTHROP 3.1 INTRODUCTION 31 3.2
DEFINITLONS 31 VI CONTENTS 3.3 IDENTIFICATION AND CONTROL WITH STATIC
NETWORKS 32 3.4 DYNAMIC NETWORKS FOR IDENTIFICATION AND CONTROL 35 3.5
STABILITY OF DYNAMIC NETWORKS 43 3.6 CONCLUSIONS 45 4. CONTROL
APPLICATIONS FOR FEEDFORWARD NETWORKS 51 G. LIGHTBODY, Q.H. WU AND G.W.
IRWIN 4.1 INTRODUCTION 51 4.2 ADAPTIVE CONTROL 51 4.3 NEURAL NETWORKS
FOR ADAPTIVE CONTROL 52 4.4 TURBOGENERATOR NEURAL CONTROL - A CASE STUDY
59 4.5 CONCLUSIONS 68 5. COMPARATIVE ASPECTS OF NEURAL NETWORKS AND
FUZZY 72 LOGIC FOR REAL-TIME CONTROL C.J. HARRIS 5.1 INTRODUCTION 72 5.2
SELF-ORGANISING FUZZY LOGIC INTELLIGENT CONTROL (SOFLIC) 74 5.3
NEUROCONTROLLERS 81 5.4 COMPARATIVE ASPECTS OF FUZZY LOGIC AND ANN S 86
5.5 APPLICATION OF SOFLIC S AND B-SPLINE NEURO-CONTROLLERS 87 TO
AUTONOMOUS LAND VEHICLE CONTROL 5.6 DISCUSSION 90 6. STUDIES IN NEURAL
NETWORK BASED CONTROL 94 K.J. HUNT AND D. SBARBARO 6.1 INTRODUCTION 94
6.2 REPRESENTATION AND IDENTIFICATION 94 6.3 GAUSSIAN NETWORKS 100 6.4
LEARNING ALGORITHMS 101 6.5 CONTROL STRUCTURES 104 6.6 EXAMPLE - PH
CONTROL 110 CONTENTS VII 7. ARTIFICIAL NEURAL NETWORKS: METHODOLOGIES
AND 123 APPLICATIONS IN PROCESS CONTROL G.A. MONTAGUE, A.J. MORRIS AND
M.J. WILLIS 7.1 INTRODUCTION 123 7.2 SYSTEM MODELLING 125 7.3
FEEDFORWARD NETWORK TRAINING AND VALIDATION 128 7.4 RADIAL BASIS
FUNCTION NETWORKS 132 7.5 COMPLEMENTARY STATISTICAL METHODS 135 7.6
STUDIES OF ARTIFICIAL NEURAL NETWORK APPROACHES 137 7.7 STATIC NETWORKS
VERSUS DYNAMIC NETWORKS 145 7.8 ARTIFICIAL NEURAL NETWORKS IN CONTROL
147 7.9 CONCLUDING REMARKS 147 8. SELECTION OF NEURAL NETWORK
STRUCTURES: SOME 151 APPROXIMATION THEORY GUIDELINES J.C. MASON AND P.C.
PARKS 8.1 INTRODUCTION 151 8.2 APPROXIMATION OF FUNCTIONS 153 8.3
CONVERGENCE OF WEIGHT TRAINING ALGORITHMS 176 8.4 CONCLUSIONS 176 9.
NEURAL NETWORKS AND SYSTEM IDENTIFICATION 181 S. BILLINGS AND S. CHEN
9.1 INTRODUCTION 181 9.2 PROBLEM FORMULATION 182 9.3 LEARNING ALGORITHMS
FOR MULTILAYERED NEURAL NETWORKS 183 9.4 RADIAL BASIS FUNCTION NETWORKS
188 9.5 THE FUNCTIONAL LINK OR EXTENDED MODEL SET NETWORK 192 9.6
PROPERTIES OF NEURAL NETWORKS 193 9.7 SYSTEM IDENTIFLCATION 200 9.8
CONCLUSIONS 202 VIII CONTENTS 10. NEURAL NETWORKS: CASE STUDIES 206 R.J.
MITCHELL AND J.M. BISHOP 10.1 INTRODUCTION 206 10.2 NEURAL NETWORK BASED
VISION SYSTEMS 206 10.3 A HYBRID NEURAL NETWORK SYSTEM 213 10.4 COLOUR
RECIPE PREDICTION USING NEURAL NETWORKS 215 10.5 KOHONEN NETWORK FOR
CHINESE SPEECH RECOGNITION 223 10.6 CONCLUSIONS 226 11. THE IMPORTANCE
OF STRUCTURE IN NEURAL NETWORKS 229 N. DODD 11.1 WHY BOTHER WITH
STRUCTURE? 229 11.2 EXAMPLES OF SPECIALISED NETWORK STRUCTURES 235 11.3
HOW DO WE FIND THE APPROPRIATE STRUCTURE? 235 11.4 DEMONSTRATION OF THE
ADVANTAGES OF AN OPTIMISED NETWORK 238 11.5 CONCLUSION 241 12. PITFALLS
IN THE APPLICATION OF NEURAL 243 NETWORKS FOR PROCESS CONTROL C. HALL
AND R. SMITH 12.1 WHY USE NEURAL NETWORKS? 243 12.2 WHERE ARE THE
PITFALLS? 244 12.3 STATISTICAL PITFALLS 244 12.4 ENGINEERING PITFALLS
252 12.5 SUMMARY 256 INDEX 257
|
| adam_txt |
NEURAL NETWORKS FOR CONTROL AND SYSTEMS EDITED BY K. WARWICK, G. W.
IRWIN AND K. J. HUNT PETER PEREGRINUS LTD. ON BEHALF OF THE INSTITUTION
OF ELECTRICAL ENGINEERS CONTENTS LIST OF CONTRIBUTORS PREFACE 1. NEURAL
NETWORKS: AN INTRODUCTION 1 K. WARWICK 1.1 INTRODUCTION 1 1.2 NEURAL
NETWORK PRINCIPLES 2 1.3 NEURAL NETWORK ELEMENTS 3 1.4 HOPFIELD NETWORKS
4 1.5 KOHONEN NETWORKS 5 1.6 BACK PROPAGATION NETWORKS 6 1.7 N-TUPLE
NETWORKS 9 1.8 CONCLUSIONS 11 2. DIGITAL NEURAL NETWORKS 13 A. REDGERS
AND I. ALEKSANDER 2.1 CLASSIFICATION OF ARTIFLCIAL NEURAL NETWORKS 13
2.2 FUNCTIONS OF BOOLEAN NODES 17 2.3 LARGE LOGIC NEURONS 20 2.4
IMPLEMENTING LARGE ANN'S USING MAGNUS 26 3. A SURVEY OF NEURAL NETWORKS
FOR CONTROL 31 R. ZBIKOWSKI AND P.J. GAWTHROP 3.1 INTRODUCTION 31 3.2
DEFINITLONS 31 VI CONTENTS 3.3 IDENTIFICATION AND CONTROL WITH STATIC
NETWORKS 32 3.4 DYNAMIC NETWORKS FOR IDENTIFICATION AND CONTROL 35 3.5
STABILITY OF DYNAMIC NETWORKS 43 3.6 CONCLUSIONS 45 4. CONTROL
APPLICATIONS FOR FEEDFORWARD NETWORKS 51 G. LIGHTBODY, Q.H. WU AND G.W.
IRWIN 4.1 INTRODUCTION 51 4.2 ADAPTIVE CONTROL 51 4.3 NEURAL NETWORKS
FOR ADAPTIVE CONTROL 52 4.4 TURBOGENERATOR NEURAL CONTROL - A CASE STUDY
59 4.5 CONCLUSIONS 68 5. COMPARATIVE ASPECTS OF NEURAL NETWORKS AND
FUZZY 72 LOGIC FOR REAL-TIME CONTROL C.J. HARRIS 5.1 INTRODUCTION 72 5.2
SELF-ORGANISING FUZZY LOGIC INTELLIGENT CONTROL (SOFLIC) 74 5.3
NEUROCONTROLLERS 81 5.4 COMPARATIVE ASPECTS OF FUZZY LOGIC AND ANN'S 86
5.5 APPLICATION OF SOFLIC'S AND B-SPLINE NEURO-CONTROLLERS 87 TO
AUTONOMOUS LAND VEHICLE CONTROL 5.6 DISCUSSION 90 6. STUDIES IN NEURAL
NETWORK BASED CONTROL 94 K.J. HUNT AND D. SBARBARO 6.1 INTRODUCTION 94
6.2 REPRESENTATION AND IDENTIFICATION 94 6.3 GAUSSIAN NETWORKS 100 6.4
LEARNING ALGORITHMS 101 6.5 CONTROL STRUCTURES 104 6.6 EXAMPLE - PH
CONTROL 110 CONTENTS VII 7. ARTIFICIAL NEURAL NETWORKS: METHODOLOGIES
AND 123 APPLICATIONS IN PROCESS CONTROL G.A. MONTAGUE, A.J. MORRIS AND
M.J. WILLIS 7.1 INTRODUCTION 123 7.2 SYSTEM MODELLING 125 7.3
FEEDFORWARD NETWORK TRAINING AND VALIDATION 128 7.4 RADIAL BASIS
FUNCTION NETWORKS 132 7.5 COMPLEMENTARY STATISTICAL METHODS 135 7.6
STUDIES OF ARTIFICIAL NEURAL NETWORK APPROACHES 137 7.7 STATIC NETWORKS
VERSUS DYNAMIC NETWORKS 145 7.8 ARTIFICIAL NEURAL NETWORKS IN CONTROL
147 7.9 CONCLUDING REMARKS 147 8. SELECTION OF NEURAL NETWORK
STRUCTURES: SOME 151 APPROXIMATION THEORY GUIDELINES J.C. MASON AND P.C.
PARKS 8.1 INTRODUCTION 151 8.2 APPROXIMATION OF FUNCTIONS 153 8.3
CONVERGENCE OF WEIGHT TRAINING ALGORITHMS 176 8.4 CONCLUSIONS 176 9.
NEURAL NETWORKS AND SYSTEM IDENTIFICATION 181 S. BILLINGS AND S. CHEN
9.1 INTRODUCTION 181 9.2 PROBLEM FORMULATION 182 9.3 LEARNING ALGORITHMS
FOR MULTILAYERED NEURAL NETWORKS 183 9.4 RADIAL BASIS FUNCTION NETWORKS
188 9.5 THE FUNCTIONAL LINK OR EXTENDED MODEL SET NETWORK 192 9.6
PROPERTIES OF NEURAL NETWORKS 193 9.7 SYSTEM IDENTIFLCATION 200 9.8
CONCLUSIONS 202 VIII CONTENTS 10. NEURAL NETWORKS: CASE STUDIES 206 R.J.
MITCHELL AND J.M. BISHOP 10.1 INTRODUCTION 206 10.2 NEURAL NETWORK BASED
VISION SYSTEMS 206 10.3 A HYBRID NEURAL NETWORK SYSTEM 213 10.4 COLOUR
RECIPE PREDICTION USING NEURAL NETWORKS 215 10.5 KOHONEN NETWORK FOR
CHINESE SPEECH RECOGNITION 223 10.6 CONCLUSIONS 226 11. THE IMPORTANCE
OF STRUCTURE IN NEURAL NETWORKS 229 N. DODD 11.1 WHY BOTHER WITH
STRUCTURE? 229 11.2 EXAMPLES OF SPECIALISED NETWORK STRUCTURES 235 11.3
HOW DO WE FIND THE APPROPRIATE STRUCTURE? 235 11.4 DEMONSTRATION OF THE
ADVANTAGES OF AN OPTIMISED NETWORK 238 11.5 CONCLUSION 241 12. PITFALLS
IN THE APPLICATION OF NEURAL 243 NETWORKS FOR PROCESS CONTROL C. HALL
AND R. SMITH 12.1 WHY USE NEURAL NETWORKS? 243 12.2 WHERE ARE THE
PITFALLS? 244 12.3 STATISTICAL PITFALLS 244 12.4 ENGINEERING PITFALLS
252 12.5 SUMMARY 256 INDEX 257 |
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| institution | BVB |
| isbn | 0863412793 |
| language | English |
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| physical | XIII, 260 S. graph. Darst. |
| publishDate | 1992 |
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| publisher | Peregrinus |
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| series | IEE control engineering series |
| series2 | IEE control engineering series |
| spelling | Neural networks for control and systems ed. by K. Warwick ... London Peregrinus 1992 XIII, 260 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier IEE control engineering series 46 Computers - Networks Inteligencia artificial (computacao) larpcal Automatic control Neural networks (Computer science) Warwick, Kevin 1954- Sonstige (DE-588)124247113 oth IEE control engineering series 46 (DE-604)BV001899902 46 GBV Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=014911361&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Neural networks for control and systems IEE control engineering series Computers - Networks Inteligencia artificial (computacao) larpcal Automatic control Neural networks (Computer science) |
| title | Neural networks for control and systems |
| title_auth | Neural networks for control and systems |
| title_exact_search | Neural networks for control and systems |
| title_exact_search_txtP | Neural networks for control and systems |
| title_full | Neural networks for control and systems ed. by K. Warwick ... |
| title_fullStr | Neural networks for control and systems ed. by K. Warwick ... |
| title_full_unstemmed | Neural networks for control and systems ed. by K. Warwick ... |
| title_short | Neural networks for control and systems |
| title_sort | neural networks for control and systems |
| topic | Computers - Networks Inteligencia artificial (computacao) larpcal Automatic control Neural networks (Computer science) |
| topic_facet | Computers - Networks Inteligencia artificial (computacao) Automatic control Neural networks (Computer science) |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=014911361&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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