Artificial neural networks for modelling and control of non-linear systems:
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Boston [u.a.]
Kluwer
1996
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XII, 235 S. graph. Darst. |
| ISBN: | 0792396782 |
Internformat
MARC
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| 100 | 1 | |a Suykens, Johan A. |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Artificial neural networks for modelling and control of non-linear systems |c by Johan A. K. Suykens ; Joos P. L. Vandewalle ; Bart L. R. DeMoor |
| 264 | 1 | |a Boston [u.a.] |b Kluwer |c 1996 | |
| 300 | |a XII, 235 S. |b graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 650 | 7 | |a Neurale netwerken |2 gtt | |
| 650 | 7 | |a Niet-lineaire systemen |2 gtt | |
| 650 | 4 | |a Neural networks (Computer science) | |
| 650 | 4 | |a Nonlinear systems | |
| 650 | 0 | 7 | |a Nichtlineares System |0 (DE-588)4042110-7 |2 gnd |9 rswk-swf |
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| 689 | 0 | 1 | |a Neuronales Netz |0 (DE-588)4226127-2 |D s |
| 689 | 0 | |5 DE-604 | |
| 700 | 1 | |a Vandewalle, Joos P. |e Verfasser |4 aut | |
| 700 | 1 | |a De Moor, Bart L. |e Verfasser |4 aut | |
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Datensatz im Suchindex
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|---|---|
| adam_text | Contents
Preface ix
Notation xi
1 Introduction 1
1.1 Neural information processing systems 1
1.2 ANNs for modelling and control 5
1.3 Chapter by Chapter overview 8
1.4 Contributions 15
2 Artificial neural networks: architectures and learning rules 19
2.1 Basic neural network architectures 19
2.2 Universal approximation theorems 23
2.2.1 Multilayer perceptrons 23
2.2.2 Radial basis function networks 27
2.3 Classical paradigms of learning 28
2.3.1 Backpropagation 29
2.3.2 RBF networks 33
2.4 Conclusion 35
3 Nonlinear system identification using neural networks 37
3.1 From linear to nonlinear dynamical models 38
3.2 Parametrization by ANNs 39
3.2.1 Input/output models 39
3.2.2 Neural state space models 41
3.2.3 Identifiability 43
3.3 Learning algorithms 45
3.3.1 Feedforward network related models 46
3.3.1.1 Backpropagation algorithm 46
3.3.1.2 Prediction error algorithms 46
v
vi Contents
3.3.1.3 Extended Kalman filtering 48
3.3.2 Recurrent network related models 50
3.3.2.1 Dynamic backpropagation 50
3.3.2.2 Extended Kalman filtering 54
3.4 Elements from nonlinear optimization theory 55
3.5 Aspects of model validation, pruning and regularization .... 58
3.6 Neural network models as uncertain linear systems 61
3.6.1 Convex polytope 62
3.6.2 LFT representation 65
3.7 Examples 68
3.7.1 Some challenging examples from the literature 68
3.7.2 Simulated nonlinear system with hysteresis 69
3.7.3 Identification of a glass furnace 75
3.7.4 Identifying 7i double scrolls 77
3.8 Conclusion 82
4 Neural networks for control 83
4.1 Neural control strategies 83
4.1.1 Direct versus indirect adaptive methods 83
4.1.2 Reinforcement learning 85
4.1.3 Neural optimal control 87
4.1.4 Internal model control and model predictive control . . 88
4.2 Neural optimal control 90
4.2.1 The TV stage optimal control problem 90
4.2.2 Neural optimal control: full state information case ... 92
4.2.3 Stabilization problem: full static state feedback 92
4.2.4 Tracking problem: the LISP principle 94
4.2.5 Dynamic backpropagation 95
4.2.6 Imposing constraints from linear control theory 96
4.2.6.1 Static feedback using feedforward nets 97
4.2.6.2 Dynamic feedback using recurrent nets .... 99
4.2.6.3 Transition between equilibrium points 101
4.2.6.4 Example: swinging up an inverted pendulum . 104
4.2.6.5 Example: swinging up a double inverted pen¬
dulum Ill
4.3 Conclusion 115
Contents vii
5 NL, Theory 117
5.1 A neural state space model framework for neural control design 118
5.2 NLg systems 122
5.3 Global asymptotic stability criteria for NL9s 127
5.3.1 Stability criteria 127
5.3.2 Discrete time Lur e problem 132
5.4 Input/Output properties I2 theory 134
5.4.1 Equivalent representations for NL?s 134
5.4.2 Main Theorems 136
5.5 Robust performance problem 140
5.5.1 Perturbed NL,s 140
5.5.2 Connections with /z theory 145
5.6 Stability analysis: formulation as LMI problems 147
5.7 Neural control design 150
5.7.1 Synthesis problem 151
5.7.2 Non convex nondifFerentiable optimization 152
5.7.3 A modified dynamic backpropagation algorithm 153
5.8 Control design: some case studies 154
5.8.1 A tracking example on diagonal scaling 154
5.8.2 A collection of stabilization problems 157
5.8.3 Mastering chaos 162
5.8.4 Controlling nonlinear distortion in loudspeakers 164
5.9 NL,s beyond control 168
5.9.1 Generalized CNNs as NLgs 168
5.9.2 LRGF networks as NL,s 172
5.10 Conclusion 175
6 General conclusions and future work 177
A Generation of rc double scrolls 181
A.I A generalization of Chua s circuit 182
A.2 ?i double scrolls 185
B Fokker Planck Learning Machine for Global Optimization 195
B.I Fokker Planck equation for recursive stochastic algorithms ... 196
B.2 Parametrization of the pdf by RBF networks 198
B.3 FP machine: conceptual algorithm 200
B.4 Examples 203
B.5 Conclusions 205
C Proof of NL, Theorems 207
viii Contents
Bibliography 215
Index 233
|
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| id | DE-604.BV010873289 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T18:00:20Z |
| institution | BVB |
| isbn | 0792396782 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-007269287 |
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| physical | XII, 235 S. graph. Darst. |
| publishDate | 1996 |
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| spelling | Suykens, Johan A. Verfasser aut Artificial neural networks for modelling and control of non-linear systems by Johan A. K. Suykens ; Joos P. L. Vandewalle ; Bart L. R. DeMoor Boston [u.a.] Kluwer 1996 XII, 235 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Neurale netwerken gtt Niet-lineaire systemen gtt Neural networks (Computer science) Nonlinear systems Nichtlineares System (DE-588)4042110-7 gnd rswk-swf Neuronales Netz (DE-588)4226127-2 gnd rswk-swf Nichtlineares System (DE-588)4042110-7 s Neuronales Netz (DE-588)4226127-2 s DE-604 Vandewalle, Joos P. Verfasser aut De Moor, Bart L. Verfasser aut HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=007269287&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Suykens, Johan A. Vandewalle, Joos P. De Moor, Bart L. Artificial neural networks for modelling and control of non-linear systems Neurale netwerken gtt Niet-lineaire systemen gtt Neural networks (Computer science) Nonlinear systems Nichtlineares System (DE-588)4042110-7 gnd Neuronales Netz (DE-588)4226127-2 gnd |
| subject_GND | (DE-588)4042110-7 (DE-588)4226127-2 |
| title | Artificial neural networks for modelling and control of non-linear systems |
| title_auth | Artificial neural networks for modelling and control of non-linear systems |
| title_exact_search | Artificial neural networks for modelling and control of non-linear systems |
| title_full | Artificial neural networks for modelling and control of non-linear systems by Johan A. K. Suykens ; Joos P. L. Vandewalle ; Bart L. R. DeMoor |
| title_fullStr | Artificial neural networks for modelling and control of non-linear systems by Johan A. K. Suykens ; Joos P. L. Vandewalle ; Bart L. R. DeMoor |
| title_full_unstemmed | Artificial neural networks for modelling and control of non-linear systems by Johan A. K. Suykens ; Joos P. L. Vandewalle ; Bart L. R. DeMoor |
| title_short | Artificial neural networks for modelling and control of non-linear systems |
| title_sort | artificial neural networks for modelling and control of non linear systems |
| topic | Neurale netwerken gtt Niet-lineaire systemen gtt Neural networks (Computer science) Nonlinear systems Nichtlineares System (DE-588)4042110-7 gnd Neuronales Netz (DE-588)4226127-2 gnd |
| topic_facet | Neurale netwerken Niet-lineaire systemen Neural networks (Computer science) Nonlinear systems Nichtlineares System Neuronales Netz |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=007269287&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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