Identification of parametric models from experimental data:
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| Main Authors: | , |
|---|---|
| Format: | Book |
| Language: | German English French |
| Published: |
London [u.a.]
Springer [u.a.]
1997
|
| Series: | Communications and control engineering series
|
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | XVIII, 413 S. graph. Darst. |
| ISBN: | 3540761195 |
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| 245 | 1 | 0 | |a Identification of parametric models from experimental data |c Éric Walter and Luc Pronzato |
| 264 | 1 | |a London [u.a.] |b Springer [u.a.] |c 1997 | |
| 300 | |a XVIII, 413 S. |b graph. Darst. | ||
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Record in the Search Index
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| adam_text | ERIC WALTER AND LUC PRONZATO IDENTIFICATION OF PARAMETRIC MODELS FROM
EXPERIMENTAL DATA TRANSLATED FROM AN UPDATED FRENCH VERSION BY THE
AUTHORS, WITH THE HELP OF JOHN NORTON SCHOOL OF ELECTRONIC AND
ELECTRICAL ENGINEERING, UNIVERSITY OF BIRMINGHAM, UK WITH 119 FIGURES .
. MASSON M SPRINGER _ PARIS MILAN BARCELONE CONTENTS NOTATION XIII 1
INTRODUCTION 1 1.1 AIMS OF MODELLING 1 1.2 SYSTEM 1 1.3 MODEL 3 1.4
CRITERION 4 1.5 OPTIMIZATION 5 1.6 PARAMETER UNCERTAINTY 5 1.7 CRITICAL
ANALYSIS OF THE RESULTS 6 1.8 IN SUMMARY 6 2 STRUCTURES 7 2.1
PHENOMENOLOGICAL AND BEHAVIOURAL MODELS 7 2.2 LINEAR AND NONLINEAR
MODELS 9 2.3 CONTINUOUS- AND DISCRETE-TIME MODELS 11 2.3.1
CONTINUOUS-TIME MODELS 11 2.3.2 DISCRETE-TIME MODELS 12 2.3.3 SAMPLING
13 2.4 DETERMINISTIC AND STOCHASTIC MODELS 15 2.5 CHOICE OF COMPLEXITY
19 2.6 STRUCTURAL PROPERTIES OF MODELS 20 2.6.1 IDENTIFIABILITY 20
2.6.1.1 LAPLACE TRANSFORM APPROACH 22 2.6.1.2 SIMILARITY TRANSFORMATION
APPROACH 24 2.6.1.3 TAYLOR SERIES APPROACH 26 2.6.1.4 LOCAL STATE
ISOMORPHISM APPROACH 28 2.6.1.5 USE OF ELIMINATION THEORY 30 2.6.1.6
NUMERICAL LOCAL APPROACH 31 2.6.2 DISTINGUISHABILITY 32 2.6.3
RELATIONSHIP BETWEEN IDENTIFIABILITY AND DISTINGUISHABILITY 34 2.6.4
CHEMICAL ENGINEERING EXAMPLE 34 2.7 CONCLUSIONS 36 3 CRITERIA 37 3.1
LEAST SQUARES 37 3.2 LEAST MODULUS :.... 39 3.3 MAXIMUM LIKELIHOOD 40
3.3.1 OUTPUT-ADDITIVE INDEPENDENT RANDOM VARIABLES 42 3.3.2
OUTPUT-ADDITIVE DEPENDENT RANDOM VARIABLES 49 3.3.3 PROPERTIES OF
MAXIMUM-LIKELIHOOD ESTIMATORS 51 3.3.4 ESTIMATION OF PARAMETER
DISTRIBUTION IN A POPULATION 53 CONTENTS 3.3.5 NONPARAMETRIC DESCRIPTION
OF STRUCTURAL ERRORS 58 3.4 COMPLEXITY 63 3.5 BAYESIAN CRITERIA 66 3.5.1
MAXIMUM A POSTERIORI 67 3.5.2 MINIMUM RISK 68 3.6 CONSTRAINTS ON
PARAMETERS 71 3.6.1 EQUALITY CONSTRAINTS 71 3.6.2 INEQUALITY CONSTRAINTS
72 3.7 ROBUSTNESS 74 3.7.1 ROBUSTNESS TO UNCERTAINTY ON THE NOISE
DISTRIBUTION 74 3.7.2 BREAKDOWN POINT 76 3.7.3 M-ESTIMATORS 77 3.7.4
IMAGE PROCESSING EXAMPLE 79 3.8 TUNING OF HYPERPARAMETERS 81 3.9
CONCLUSIONS 82 4 OPTIMIZATION 83 4.1 LP STRUCTURES AND QUADRATIC COST
FUNCTIONS 84 4.1.1 LP STRUCTURES 84 4.1.2 QUADRATIC COST FUNCTIONS 88
4.1.3 LEAST-SQUARES ESTIMATOR 88 4.1.3.1 PROPERTIES OF THE LEAST-SQUARES
ESTIMATOR 89 4.1.3.2 NUMERICAL CONSIDERATIONS 91 4.1.4 DATA-RECURSIVE
LEAST SQUARES 92 4.1.4.1 P* ASSUMED TO BE CONSTANT 92 4.1.4.2 P* MAY
DRIFT 96 4.1.4.3 P* MAY JUMP 97 4.1.4.4 APPLICATION TO ADAPTIVE CONTROL
97 4.1.5 PARAMETER-RECURSIVE LEAST SQUARES 101 4.1.6 KALMAN FILTER 102
4.1.6.1 VECTOR DATA-RECURSIVE LEAST SQUARES 104 4.1.6.2 STATIC SYSTEM
WITHOUT PROCESS NOISE 105 4.1.6.3 DYNAMIC SYSTEM WITH PROCESS NOISE 106
4.1.6.4 OFF-LINE COMPUTATION 108 4.1.6.5 ON-LINE COMPUTATION 108 4.1.6.6
INFLUENCE OF THE COVARIANCES OF THE PROCESS AND MEASUREMENT NOISE 108 --
4.1.6.7 DETECTION OF DIVERGENCE 109 4.1.6.8 STATIONARY FILTER 110
4.1.6.9 USE FOR THE CHOICE OF SENSORS ILL 4.1.6.10 EXTENDED KALMAN
FILTER: REAL-TIME PARAMETER ESTIMATION 112 4.1.6.11 STOCHASTIC
IDENTIFICATION 114 4.1.7 ERRORS-IN-VARIABLES APPROACH 115 4.2
LEAST-SQUARES BASED METHODS 117 4.2.1 PSEUDOLINEAR REGRESSION 118
4.2.1.1 EXTENDED LEAST SQUARES 118 4.2.1.2 PROPERTIES OF EXTENDED LEAST
SQUARES 119 4.2.2 MULTILINEAR REGRESSION 119 4.2.2.1 GENERALIZED LEAST
SQUARES 120 4.2.2.2 PROPERTIES OF GENERALIZED LEAST SQUARES 121 4.2.3
FILTERING 122 4.2.3.1 STEIGLITZ AND MCBRIDE S METHOD 122 4.2.3.2
EXTENDED MATRIX METHOD 125 4.2.4 FIRST-ORDER EXPANSION OF THE ERROR 126
4.2.5 INSTRUMENTAL-VARIABLE METHOD 127 CONTENTS 4.2.6 LEAST SQUARES ON
CORRELATIONS 129 4.3 GENERAL METHODS 130 4.3.1 QUADRATIC COST AND
PARTIALLY LP STRUCTURE 131 4.3.2 ONE-DIMENSIONAL OPTIMIZATION 131
4.3.2.1 DEFINITION OF A SEARCH INTERVAL 132 4.3.2.2 DICHOTOMY 133
4.3.2.3 FIBONACCI S AND GOLDEN-SECTION METHODS 133 4.3.2.4 PARABOLIC
INTERPOLATION 136 4.3.2.5 WHICH METHOD? 137 4.3.2.6 COMBINING
ONE-DIMENSIONAL OPTIMIZATIONS 137 4.3.3 LIMITED EXPANSIONS OF THE COST
142 4.3.3.1 GRADIENT METHOD 142 4.3.3.2 COMPUTATION OF THE GRADIENT 149
4.3.3.3 NEWTON METHOD 167 4.3.3.4 GAUSS-NEWTON METHOD 171 4.3.3.5
LEVENBERG-MARQUARDT METHOD 173 4.3.3.6 QUASI-NEWTON METHODS 174 4.3.3.7
HEAVY-BALL METHOD 178 4.3.3.8 CONJUGATE-GRADIENT METHODS 178 4.3.3.9
CHOICE OF STEP SIZE 182 4.3.4 CONSTRAINED OPTIMIZATION 184 4.3.4.1
LINEAR PROGRAMMING 185 4.3.4.2 QUADRATIC PROGRAMMING 187 4.3.4.3
CONSTRAINED GRADIENT 189 4.3.4.4 GRADIENT-PROJECTION METHOD 190 4.3.4.5
CONSTRAINED NEWTON AND QUASI-NEWTON 192 4.3.4.6 METHOD OF CENTRES 194
4.3.4.7 METHOD OF FEASIBLE DIRECTIONS 196 4.3.5 NON-DIFFERENTIABLE COST
FUNCTIONS 197 4.3.5.1 SUBGRADIENT METHOD 197 4.3.5.2 CUTTING-PLANE
METHOD 199 4.3.5.3 ELLIPSOIDAL METHOD 200 4.3.5.4 APPLICATION TO L
ESTIMATION 201 4.3.6 INITIALISATION 203 4.3.7 TERMINATION 204 4.3.8
RECURSIVE TECHNIQUES 206 4.3.9 GLOBAL OPTIMIZATION 211 4.3.9.1
ELIMINATING PARASITIC LOCAL OPTIMA 211 4.3.9.2 RANDOM SEARCH 216 4.3.9.3
DETERMINISTIC SEARCH 219 4.4 OPTIMIZATION OF A MEASURED RESPONSE 226
4.4.1 MODEL-FREE OPTIMIZATION 226 4.4.2 RESPONSE-SURFACE METHODOLOGY 227
4.5 CONCLUSIONS 229 UNCERTAINTY 231 5.1 COST CONTOURS IN PARAMETER SPACE
231 5.1.1 NORMAL NOISE: COST CONTOURS, CONFIDENCE REGIONS 231 5.1.1.1
NOISE WITH KNOWN VARIANCE 232 5.1.1.2 NOISE WITH UNKNOWN VARIANCE 235
5.1.1.3 NOISE WITH INDEPENDENTLY ESTIMATED VARIANCE 237 5.1.2
DETERMINATION OF POINTS ON A COST CONTOUR 238 5.1.3 CHARACTERIZATION OF
NON-CONNECTED DOMAINS 240 5.1.4 REPRESENTATION OF COST CONTOURS 240
CONTENTS 5.2 MONTE-CARLO METHODS 242 5.2.1 PRINCIPLE 242 5.2.2 NUMBER OF
SIGNIFICANT DIGITS OF THE ESTIMATE: THE CESTAC METHOD 243 5.2.3
GENERATING FICTITIOUS DATA BY JACK-KNIFE AND BOOTSTRAP 243 --5.2.3.1
.JACK-KNIFE 244 5.2.3.2 BOOTSTRAP 244 5.3 METHODS BASED ON THE DENSITY
OF THE ESTIMATOR 245 5.3.1 NON-BAYESIAN ESTIMATORS 245 5.3.1.1
CRAMER-RAO INEQUALITY 246 5.3.1.2 LP MODEL STRUCTURE AND NORMAL NOISE
WITH KNOWN COVARIANCE 246 5.3.1.3 LP MODEL STRUCTURE AND NORMAL NOISE
WITH UNKNOWN VARIANCE 249 5.3.1.4 OTHER CASES 250 5.3.2 BAYESIAN
ESTIMATORS 253 5.3.3 APPROXIMATION OF THE PROBABILITY DENSITY OF THE
ESTIMATOR 254 5.4 BOUNDED-ERROR SET ESTIMATION 257 5.4.1 LP MODEL
STRUCTURES 259 5.4.1.1 RECURSIVE DETERMINATION OF OUTER ELLIPSOIDS 260
5.4.1.2 NON-RECURSIVE DETERMINATION OF OUTER BOXES 269 5.4.1.3 EXACT
DESCRIPTION 270 5.4.2 NON-LP MODEL STRUCTURES 272 5.4.2.1 ERRORS IN
VARIABLES 274 5.4.2.2 OUTLIER MINIMAL NUMBER ESTIMATOR 276 5.4.2.3 SET
INVERSION 280 5.5 CONCLUSIONS 283 6 EXPERIMENTS 285 6.1 CRITERIA 287 6.2
LOCAL DESIGN 291 6.2.1 EXACT DESIGN 292 6.2.1.1 FEDOROV S ALGORITHM 292
6.2.1.2 DETMAX ALGORITHM 294 6.2.2 DISTRIBUTION OF EXPERIMENTAL EFFORT
295 6.2.2.1 CONTINUOUS DESIGN 295 6.2.2.2 APPROXIMATE DESIGN 296 6.2.2.3
PROPERTIES OF OPTIMAL EXPERIMENTS 297 6.2.2.4 ALGORITHMS 303 6.3
APPLICATIONS 306 6.3.1 OPTIMAL MEASUREMENT TIMES 306 6.3.2 OPTIMAL
INPUTS 306 6.3.2.1 PARAMETRIC INPUTS 307 6.3.2.2 NONPARAMETRIC INPUTS
308 6.3.3 SIMULTANEOUS CHOICE OF INPUTS AND SAMPLING TIMES 326 6.4
ROBUST DESIGN 329 6.4.1 LIMITATIONS OF LOCAL DESIGN 329 6.4.2 SEQUENTIAL
DESIGN 331 6.4.3 AVERAGE OPTIMALITY 333 6.4.3.1 CRITERIA 333 6.4.3.2
ALGORITHMS 336 6.4.4 MINIMAX OPTIMALITY 338 6.4.4.1 CRITERIA 338 6.4.4.2
ALGORITHMS 340 6.5 DESIGN FOR BAYESIAN ESTIMATION 342 6.5.1 EXACT DESIGN
343 6.5.2 APPROXIMATE DESIGN 344 CONTENTS 6.6 INFLUENCE OF MODEL
STRUCTURE 345 6.6.1 ROBUSTNESS THROUGH BAYESIAN ESTIMATION 346 6.6.2
ROBUST ESTIMATION AND DESIGN 347 6.6.2.1 MINIMAX APPROACH 348 6.6.2.2
BAYESIAN APPROACH 349 6.6.3 STRUCTURE DISCRIMINATION 349 6.6.3.1
DISCRIMINATING BY PREDICTION DISCREPANCY 350 6.6.3.2 DISCRIMINATING VIA
ENTROPY 352 6.6.3.3 DISCRIMINATING VIA D S -OPTIMALITY 354 6.6.3.4
POSSIBLE EXTENSIONS 355 6.7 CONCLUSIONS 356 7 FALSIFICATION 359 7.1
SIMPLE INSPECTION 359 7.2 STATISTICAL ANALYSIS OF RESIDUALS 360 7.2.1
TESTING FOR NORMALITY 362 7.2.2 TESTING FOR STATIONARITY 372 7.2.3
TESTING FOR INDEPENDENCE 376 7.3 CONCLUSIONS 380 REFERENCES 381 INDEX
405
|
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| author | Walter, Eric 1950- Pronzato, Luc |
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| author_facet | Walter, Eric 1950- Pronzato, Luc |
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| dewey-full | 620.00151 003.52 |
| dewey-hundreds | 600 - Technology (Applied sciences) 000 - Computer science, information, general works |
| dewey-ones | 620 - Engineering and allied operations 003 - Systems |
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| dewey-search | 620.001 51 003.52 |
| dewey-sort | 3620.001 251 |
| dewey-tens | 620 - Engineering and allied operations 000 - Computer science, information, general works |
| discipline | Informatik Mathematik Mess-/Steuerungs-/Regelungs-/Automatisierungstechnik |
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| id | DE-604.BV011107954 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T18:04:07Z |
| institution | BVB |
| isbn | 3540761195 |
| language | German English French |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-007442613 |
| oclc_num | 468753733 |
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| owner | DE-703 DE-91 DE-BY-TUM DE-29T DE-706 |
| owner_facet | DE-703 DE-91 DE-BY-TUM DE-29T DE-706 |
| physical | XVIII, 413 S. graph. Darst. |
| publishDate | 1997 |
| publishDateSearch | 1997 |
| publishDateSort | 1997 |
| publisher | Springer [u.a.] |
| record_format | marc |
| series2 | Communications and control engineering series |
| spelling | Walter, Eric 1950- Verfasser (DE-588)115525394 aut Identification de modèles paramétriques à partir de données expérimentales Identification of parametric models from experimental data Éric Walter and Luc Pronzato London [u.a.] Springer [u.a.] 1997 XVIII, 413 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Communications and control engineering series Estimation, Théorie de l' ram Systèmes - Identification - Modèles mathématiques ram Systemidentifikation (DE-588)4121753-6 gnd rswk-swf Parameterschätzung (DE-588)4044614-1 gnd rswk-swf Systemidentifikation (DE-588)4121753-6 s Parameterschätzung (DE-588)4044614-1 s DE-604 Pronzato, Luc Verfasser aut GBV Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=007442613&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Walter, Eric 1950- Pronzato, Luc Identification of parametric models from experimental data Estimation, Théorie de l' ram Systèmes - Identification - Modèles mathématiques ram Systemidentifikation (DE-588)4121753-6 gnd Parameterschätzung (DE-588)4044614-1 gnd |
| subject_GND | (DE-588)4121753-6 (DE-588)4044614-1 |
| title | Identification of parametric models from experimental data |
| title_alt | Identification de modèles paramétriques à partir de données expérimentales |
| title_auth | Identification of parametric models from experimental data |
| title_exact_search | Identification of parametric models from experimental data |
| title_full | Identification of parametric models from experimental data Éric Walter and Luc Pronzato |
| title_fullStr | Identification of parametric models from experimental data Éric Walter and Luc Pronzato |
| title_full_unstemmed | Identification of parametric models from experimental data Éric Walter and Luc Pronzato |
| title_short | Identification of parametric models from experimental data |
| title_sort | identification of parametric models from experimental data |
| topic | Estimation, Théorie de l' ram Systèmes - Identification - Modèles mathématiques ram Systemidentifikation (DE-588)4121753-6 gnd Parameterschätzung (DE-588)4044614-1 gnd |
| topic_facet | Estimation, Théorie de l' Systèmes - Identification - Modèles mathématiques Systemidentifikation Parameterschätzung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=007442613&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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