Filtering and system identification: a least squares approach
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cambridge
Cambridge University Press
2007
|
| Schlagworte: | |
| Online-Zugang: | Contributor biographical information Publisher description Table of contents only Table of contents Inhaltsverzeichnis |
| Beschreibung: | Includes bibliographical references (p. 395-400) and index |
| Beschreibung: | XV, 405 S. Ill. 25 cm |
| ISBN: | 0521875129 |
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| 245 | 1 | 0 | |a Filtering and system identification |b a least squares approach |c Michel Verhaegen, Vincent Verdult |
| 264 | 1 | |a Cambridge |b Cambridge University Press |c 2007 | |
| 300 | |a XV, 405 S. |b Ill. |c 25 cm | ||
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Datensatz im Suchindex
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| adam_text | FILTERING AND SYSTEM IDENTIFICATION A LEAST SQUARES APPROACH MICHEL
VERHAEGEN TECHNISCHE UNIVERSITEIT DELFT, THE NETHERLANDS VINCENT VERDULT
TECHNISCHE UNIVERSITEIT DELFT, THE NETHERLANDS I CAMBRIDGE UNIVERSITY
PRESS CONTENTS PREFACE PAGE XI NOTATION AND SYMBOLS XIII LIST OF
ABBREVIATIONS XV 1 INTRODUCTION 1 2 LINEAR ALGEBRA 8 2.1 INTRODUCTION 8
2.2 VECTORS 9 2.3 MATRICES 13 2.4 SQUARE MATRICES 18 2.5 MATRIX
DECOMPOSITIONS 25 2.6 LINEAR LEAST-SQUARES PROBLEMS 28 2.6.1 SOLUTION IF
THE MATRIX F HAS FULL COLUMN RANK 32 2.6.2 SOLUTIONS IF THE MATRIX F
DOES NOT HAVE FULL COLUMN RANK 33 2.7 WEIGHTED LINEAR LEAST-SQUARES
PROBLEMS 35 2.8 SUMMARY 37 3 DISCRETE-TIME SIGNALS AND SYSTEMS 42 3.1
INTRODUCTION 42 3.2 SIGNALS * 43 3.3 SIGNAL TRANSFORMS , 47 3.3.1 THE Z-
TRANSFORM 47 3.3.2 THE DISCRETE-TIME FOURIER TRANSFORM 50 3.4 LINEAR
SYSTEMS 55 3.4.1 LINEARIZATION 58 3.4.2 SYSTEM RESPONSE AND STABILITY 59
3.4.3 CONTROLLABILITY AND OBSERVABILITY 64 3.4.4 INPUT-OUTPUT
DESCRIPTIONS 69 VI CONTENTS 3.5 INTERACTION BETWEEN SYSTEMS 3.6 SUMMARY
4 RANDOM VARIABLES AND SIGNALS 4.1 INTRODUCTION 4.2 DESCRIPTION OF A
RANDOM VARIABLE 4.2.1 EXPERIMENTS AND EVENTS 4.2.2 THE PROBABILITY MODEL
4.2.3 LINEAR FUNCTIONS OF A RANDOM VARIABLE 4.2.4 THE EXPECTED VALUE OF
A RANDOM VARIABLE 4.2.5 GAUSSIAN RANDOM VARIABLES 4.2.6 MULTIPLE RANDOM
VARIABLES 4.3 RANDOM SIGNALS 4.3.1 EXPECTATIONS OF RANDOM SIGNALS 4.3.2
IMPORTANT CLASSES OF RANDOM SIGNALS 4.3.3 STATIONARY RANDOM SIGNALS
4.3.4 ERGODICITY AND TIME AVERAGES OF RANDOM SIGNALS 4.4 POWER SPECTRA
4.5 PROPERTIES OF LEAST-SQUARES ESTIMATES 4.5.1 THE LINEAR LEAST-SQUARES
PROBLEM 4.5.2 THE WEIGHTED LINEAR LEAST-SQUARES PROBLEM 4.5.3 THE
STOCHASTIC LINEAR LEAST-SQUARES PROBLEM 4.5.4 A SQUARE-ROOT SOLUTION TO
THE STOCHASTIC LINEAR LEAST-SQUARES PROBLEM 4.5.5 MAXIMUM-LIKELIHOOD
INTERPRETATION OF THE WEIGHTED LINEAR LEAST-SQUARES PROBLEM 4.6 SUMMARY
5 KALMAN FILTERING 5.1 INTRODUCTION 5.2 THE ASYMPTOTIC OBSERVER 5.3 THE
KALMAN-FILTER PROBLEM * 5.4 THE KALMAN FILTER AND STOCHASTIC LEAST
SQUARES 5.5 THE KALMAN FILTER AND WEIGHTED LEAST SQUARES 5.5.1 A
WEIGHTED LEAST-SQUARES PROBLEM FORMULATION 141 5.5.2 THE MEASUREMENT
UPDATE 5.5.3 THE TIME UPDATE 5.5.4 THE COMBINED MEASUREMENT-TIME UPDATE
5.5.5 THE INNOVATION FORM REPRESENTATION 5.6 FIXED-INTERVAL SMOOTHING
CONTENTS VII 5.7 THE KALMAN FILTER FOR LTI SYSTEMS 162 5.8 THE KALMAN
FILTER FOR ESTIMATING UNKNOWN INPUTS 166 5.9 SUMMARY 171 ESTIMATION OF
SPECTRA AND FREQUENCY-RESPONSE FUNCTIONS 178 6.1 INTRODUCTION 178 6.2
THE DISCRETE FOURIER TRANSFORM 180 6.3 SPECTRAL LEAKAGE 185 6.4 THE FFT
ALGORITHM 188 6.5 ESTIMATION OF SIGNAL SPECTRA 191 6.6 ESTIMATION OF
FRFS AND DISTURBANCE SPECTRA 195 6.6.1 PERIODIC INPUT SEQUENCES 196
6.6.2 GENERAL INPUT SEQUENCES 198 6.6.3 ESTIMATING THE DISTURBANCE
SPECTRUM 200 6.7 SUMMARY 203 OUTPUT-ERROR PARAMETRIC MODEL ESTIMATION
207 7.1 INTRODUCTION 207 7.2 PROBLEMS IN ESTIMATING PARAMETERS OF AN LTI
STATE-SPACE MODEL 209 7.3 PARAMETERIZING A MIMO LTI STATE-SPACE MODEL
213 7.3.1 THE OUTPUT NORMAL FORM 219 7.3.2 THE TRIDIAGONAL FORM 226 7.4
THE OUTPUT-ERROR COST FUNCTION 227 7.5 NUMERICAL PARAMETER ESTIMATION
231 7.5.1 THE GAUSS-NEWTON METHOD 233 7.5.2 REGULARIZATION IN THE
GAUSS-NEWTON METHOD 237 7.5.3 THE STEEPEST DESCENT METHOD 237 7.5.4
GRADIENT PROJECTION 239 7.6 ANALYZING THE ACCURACY OF THE ESTIMATES 242
7.7 DEALING WITH COLORED MEASUREMENT NOISE 245 7.7.1 WEIGHTED LEAST
SQUARES * 247 7.7.2 PREDICTION-ERROR METHODS 248 7.8 SUMMARY 248
PREDICTION-ERROR PARAMETRIC MODEL ESTIMATION 254 8.1 INTRODUCTION 294
8.2 PREDICTION-ERROR METHODS FOR ESTIMATING STATE-SPACE MODELS 256 8.2.1
PARAMETERIZING AN INNOVATION STATE-SPACE MODEL 257 VIII CONTENTS 8.2.2
THE PREDICTION-ERROR COST FUNCTION 8.2.3 NUMERICAL PARAMETER ESTIMATION
8.2.4 ANALYZING THE ACCURACY OF THE ESTIMATES 8.3 SPECIFIC MODEL
PARAMETERIZATIONS FOR SISO SYSTEMS 8.3.1 THE ARM AX AND ARX MODEL
STRUCTURES 8.3.2 THE BOX-JENKINS AND OUTPUT-ERROR MODEL STRUCTURES 8.4
QUALITATIVE ANALYSIS OF THE MODEL BIAS FOR SISO SYSTEMS 8.5 ESTIMATION
PROBLEMS IN CLOSED-LOOP SYSTEMS 8.6 SUMMARY 9 SUBSPACE MODEL
IDENTIFICATION 9.1 INTRODUCTION 9.2 SUBSPACE MODEL IDENTIFICATION FOR
DETERMINISTIC SYSTEMS 9.2.1 THE DATA EQUATION 9.2.2 IDENTIFICATION FOR
AUTONOMOUS SYSTEMS 9.2.3 IDENTIFICATION USING IMPULSE INPUT SEQUENCES
9.2.4 IDENTIFICATION USING GENERAL INPUT SEQUENCES 9.3 SUBSPACE
IDENTIFICATION WITH WHITE MEASUREMENT NOISE 9.4 THE USE OF INSTRUMENTAL
VARIABLES 9.5 SUBSPACE IDENTIFICATION WITH COLORED MEASUREMENT NOISE 9.6
SUBSPACE IDENTIFICATION WITH PROCESS AND MEASUREMENT NOISE 9.6.1 THE
PO-MOESP METHOD 9.6.2 SUBSPACE IDENTIFICATION AS A LEAST-SQUARES PROBLEM
9.6.3 ESTIMATING THE KALMAN GAIN KT 9.6.4 RELATIONS AMONG DIFFERENT
SUBSPACE * IDENTIFICATION METHODS 9.7 USING SUBSPACE IDENTIFICATION WITH
CLOSED-LOOP DATA 9.8 SUMMARY 10 THE SYSTEM-IDENTIFICATION CYCLE 10.1
INTRODUCTION 10.2 EXPERIMENT DESIGN 10.2.1 CHOICE OF SAMPLING FREQUENCY
10.2.2 TRANSIENT-RESPONSE ANALYSIS CONTENTS IX 10.2.3 EXPERIMENT
DURATION 355 10.2.4 PERSISTENCY OF EXCITATION OF THE INPUT SEQUENCE 356
10.2.5 TYPES OF INPUT SEQUENCE 366 10.3 DATA PRE-PROCESSING 369 10.3.1
DECIMATION 369 10.3.2 DETRENDING THE DATA 370 10.3.3 PRE-FILTERING THE
DATA 372 10.3.4 CONCATENATING DATA SEQUENCES 373 10.4 SELECTION OF THE
MODEL STRUCTURE 373 10.4.1 DELAY ESTIMATION 373 10.4.2 MODEL-STRUCTURE
SELECTION IN ARMAX MODEL ESTIMATION 376 10.4.3 MODEL-STRUCTURE SELECTION
IN SUBSPACE IDENTIFICATION 382 10.5 MODEL VALIDATION 387 10.5.1 THE
AUTO-CORRELATION TEST 388 10.5.2 THE CROSS-CORRELATION TEST 388 10.5.3
THE CROSS-VALIDATION TEST 390 10.6 SUMMARY 390 REFERENCES 395 INDEX 401
|
| adam_txt |
FILTERING AND SYSTEM IDENTIFICATION A LEAST SQUARES APPROACH MICHEL
VERHAEGEN TECHNISCHE UNIVERSITEIT DELFT, THE NETHERLANDS VINCENT VERDULT
TECHNISCHE UNIVERSITEIT DELFT, THE NETHERLANDS I CAMBRIDGE UNIVERSITY
PRESS CONTENTS PREFACE PAGE XI NOTATION AND SYMBOLS XIII LIST OF
ABBREVIATIONS XV 1 INTRODUCTION 1 2 LINEAR ALGEBRA 8 2.1 INTRODUCTION 8
2.2 VECTORS 9 2.3 MATRICES 13 2.4 SQUARE MATRICES 18 2.5 MATRIX
DECOMPOSITIONS 25 2.6 LINEAR LEAST-SQUARES PROBLEMS 28 2.6.1 SOLUTION IF
THE MATRIX F HAS FULL COLUMN RANK 32 2.6.2 SOLUTIONS IF THE MATRIX F
DOES NOT HAVE FULL COLUMN RANK 33 2.7 WEIGHTED LINEAR LEAST-SQUARES
PROBLEMS 35 2.8 SUMMARY 37 3 DISCRETE-TIME SIGNALS AND SYSTEMS 42 3.1
INTRODUCTION 42 3.2 SIGNALS * 43 3.3 SIGNAL TRANSFORMS , 47 3.3.1 THE Z-
TRANSFORM 47 3.3.2 THE DISCRETE-TIME FOURIER TRANSFORM 50 3.4 LINEAR
SYSTEMS 55 3.4.1 LINEARIZATION 58 3.4.2 SYSTEM RESPONSE AND STABILITY 59
3.4.3 CONTROLLABILITY AND OBSERVABILITY 64 3.4.4 INPUT-OUTPUT
DESCRIPTIONS 69 VI CONTENTS 3.5 INTERACTION BETWEEN SYSTEMS 3.6 SUMMARY
4 RANDOM VARIABLES AND SIGNALS 4.1 INTRODUCTION 4.2 DESCRIPTION OF A
RANDOM VARIABLE 4.2.1 EXPERIMENTS AND EVENTS 4.2.2 THE PROBABILITY MODEL
4.2.3 LINEAR FUNCTIONS OF A RANDOM VARIABLE 4.2.4 THE EXPECTED VALUE OF
A RANDOM VARIABLE 4.2.5 GAUSSIAN RANDOM VARIABLES 4.2.6 MULTIPLE RANDOM
VARIABLES 4.3 RANDOM SIGNALS 4.3.1 EXPECTATIONS OF RANDOM SIGNALS 4.3.2
IMPORTANT CLASSES OF RANDOM SIGNALS 4.3.3 STATIONARY RANDOM SIGNALS
4.3.4 ERGODICITY AND TIME AVERAGES OF RANDOM SIGNALS 4.4 POWER SPECTRA
4.5 PROPERTIES OF LEAST-SQUARES ESTIMATES 4.5.1 THE LINEAR LEAST-SQUARES
PROBLEM 4.5.2 THE WEIGHTED LINEAR LEAST-SQUARES PROBLEM 4.5.3 THE
STOCHASTIC LINEAR LEAST-SQUARES PROBLEM 4.5.4 A SQUARE-ROOT SOLUTION TO
THE STOCHASTIC LINEAR LEAST-SQUARES PROBLEM 4.5.5 MAXIMUM-LIKELIHOOD
INTERPRETATION OF THE WEIGHTED LINEAR LEAST-SQUARES PROBLEM 4.6 SUMMARY
5 KALMAN FILTERING 5.1 INTRODUCTION 5.2 THE ASYMPTOTIC OBSERVER 5.3 THE
KALMAN-FILTER PROBLEM * 5.4 THE KALMAN FILTER AND STOCHASTIC LEAST
SQUARES 5.5 THE KALMAN FILTER AND WEIGHTED LEAST SQUARES 5.5.1 A
WEIGHTED LEAST-SQUARES PROBLEM FORMULATION 141 5.5.2 THE MEASUREMENT
UPDATE 5.5.3 THE TIME UPDATE 5.5.4 THE COMBINED MEASUREMENT-TIME UPDATE
5.5.5 THE INNOVATION FORM REPRESENTATION 5.6 FIXED-INTERVAL SMOOTHING
CONTENTS VII 5.7 THE KALMAN FILTER FOR LTI SYSTEMS 162 5.8 THE KALMAN
FILTER FOR ESTIMATING UNKNOWN INPUTS 166 5.9 SUMMARY 171 ESTIMATION OF
SPECTRA AND FREQUENCY-RESPONSE FUNCTIONS 178 6.1 INTRODUCTION 178 6.2
THE DISCRETE FOURIER TRANSFORM 180 6.3 SPECTRAL LEAKAGE 185 6.4 THE FFT
ALGORITHM 188 6.5 ESTIMATION OF SIGNAL SPECTRA 191 6.6 ESTIMATION OF
FRFS AND DISTURBANCE SPECTRA 195 6.6.1 PERIODIC INPUT SEQUENCES 196
6.6.2 GENERAL INPUT SEQUENCES 198 6.6.3 ESTIMATING THE DISTURBANCE
SPECTRUM 200 6.7 SUMMARY 203 OUTPUT-ERROR PARAMETRIC MODEL ESTIMATION
207 7.1 INTRODUCTION 207 7.2 PROBLEMS IN ESTIMATING PARAMETERS OF AN LTI
STATE-SPACE MODEL 209 7.3 PARAMETERIZING A MIMO LTI STATE-SPACE MODEL
213 7.3.1 THE OUTPUT NORMAL FORM 219 7.3.2 THE TRIDIAGONAL FORM 226 7.4
THE OUTPUT-ERROR COST FUNCTION 227 7.5 NUMERICAL PARAMETER ESTIMATION
231 7.5.1 THE GAUSS-NEWTON METHOD 233 7.5.2 REGULARIZATION IN THE
GAUSS-NEWTON METHOD 237 7.5.3 THE STEEPEST DESCENT METHOD 237 7.5.4
GRADIENT PROJECTION 239 7.6 ANALYZING THE ACCURACY OF THE ESTIMATES 242
7.7 DEALING WITH COLORED MEASUREMENT NOISE 245 7.7.1 WEIGHTED LEAST
SQUARES * 247 7.7.2 PREDICTION-ERROR METHODS 248 7.8 SUMMARY ' 248
PREDICTION-ERROR PARAMETRIC MODEL ESTIMATION 254 8.1 INTRODUCTION 294
8.2 PREDICTION-ERROR METHODS FOR ESTIMATING STATE-SPACE MODELS 256 8.2.1
PARAMETERIZING AN INNOVATION STATE-SPACE MODEL 257 VIII CONTENTS 8.2.2
THE PREDICTION-ERROR COST FUNCTION 8.2.3 NUMERICAL PARAMETER ESTIMATION
8.2.4 ANALYZING THE ACCURACY OF THE ESTIMATES 8.3 SPECIFIC MODEL
PARAMETERIZATIONS FOR SISO SYSTEMS 8.3.1 THE ARM AX AND ARX MODEL
STRUCTURES 8.3.2 THE BOX-JENKINS AND OUTPUT-ERROR MODEL STRUCTURES 8.4
QUALITATIVE ANALYSIS OF THE MODEL BIAS FOR SISO SYSTEMS 8.5 ESTIMATION
PROBLEMS IN CLOSED-LOOP SYSTEMS 8.6 SUMMARY 9 SUBSPACE MODEL
IDENTIFICATION 9.1 INTRODUCTION 9.2 SUBSPACE MODEL IDENTIFICATION FOR
DETERMINISTIC SYSTEMS 9.2.1 THE DATA EQUATION 9.2.2 IDENTIFICATION FOR
AUTONOMOUS SYSTEMS 9.2.3 IDENTIFICATION USING IMPULSE INPUT SEQUENCES
9.2.4 IDENTIFICATION USING GENERAL INPUT SEQUENCES 9.3 SUBSPACE
IDENTIFICATION WITH WHITE MEASUREMENT NOISE 9.4 THE USE OF INSTRUMENTAL
VARIABLES 9.5 SUBSPACE IDENTIFICATION WITH COLORED MEASUREMENT NOISE 9.6
SUBSPACE IDENTIFICATION WITH PROCESS AND MEASUREMENT NOISE 9.6.1 THE
PO-MOESP METHOD 9.6.2 SUBSPACE IDENTIFICATION AS A LEAST-SQUARES PROBLEM
9.6.3 ESTIMATING THE KALMAN GAIN KT 9.6.4 RELATIONS AMONG DIFFERENT
SUBSPACE * IDENTIFICATION METHODS 9.7 USING SUBSPACE IDENTIFICATION WITH
CLOSED-LOOP DATA 9.8 SUMMARY 10 THE SYSTEM-IDENTIFICATION CYCLE 10.1
INTRODUCTION 10.2 EXPERIMENT DESIGN 10.2.1 CHOICE OF SAMPLING FREQUENCY
10.2.2 TRANSIENT-RESPONSE ANALYSIS CONTENTS IX 10.2.3 EXPERIMENT
DURATION 355 10.2.4 PERSISTENCY OF EXCITATION OF THE INPUT SEQUENCE 356
10.2.5 TYPES OF INPUT SEQUENCE 366 10.3 DATA PRE-PROCESSING 369 10.3.1
DECIMATION 369 10.3.2 DETRENDING THE DATA 370 10.3.3 PRE-FILTERING THE
DATA 372 10.3.4 CONCATENATING DATA SEQUENCES 373 10.4 SELECTION OF THE
MODEL STRUCTURE 373 10.4.1 DELAY ESTIMATION 373 10.4.2 MODEL-STRUCTURE
SELECTION IN ARMAX MODEL ESTIMATION 376 10.4.3 MODEL-STRUCTURE SELECTION
IN SUBSPACE IDENTIFICATION 382 10.5 MODEL VALIDATION 387 10.5.1 THE
AUTO-CORRELATION TEST 388 10.5.2 THE CROSS-CORRELATION TEST 388 10.5.3
THE CROSS-VALIDATION TEST 390 10.6 SUMMARY 390 REFERENCES 395 INDEX 401 |
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| author | Verhaegen, Michel Verdult, Vincent |
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| callnumber-subject | QA - Mathematics |
| classification_rvk | SK 960 |
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| ctrlnum | (OCoLC)255543584 (DE-599)BVBBV023399520 |
| dewey-full | 003.1 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 003 - Systems |
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| dewey-sort | 13.1 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik Mathematik Mess-/Steuerungs-/Regelungs-/Automatisierungstechnik |
| discipline_str_mv | Informatik Mathematik Mess-/Steuerungs-/Regelungs-/Automatisierungstechnik |
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| id | DE-604.BV023399520 |
| illustrated | Illustrated |
| index_date | 2024-07-02T21:23:10Z |
| indexdate | 2024-07-09T21:17:45Z |
| institution | BVB |
| isbn | 0521875129 |
| language | English |
| lccn | 2007298771 |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-016582328 |
| oclc_num | 255543584 |
| open_access_boolean | |
| owner | DE-19 DE-BY-UBM DE-91G DE-BY-TUM |
| owner_facet | DE-19 DE-BY-UBM DE-91G DE-BY-TUM |
| physical | XV, 405 S. Ill. 25 cm |
| publishDate | 2007 |
| publishDateSearch | 2007 |
| publishDateSort | 2007 |
| publisher | Cambridge University Press |
| record_format | marc |
| spelling | Verhaegen, Michel Verfasser aut Filtering and system identification a least squares approach Michel Verhaegen, Vincent Verdult Cambridge Cambridge University Press 2007 XV, 405 S. Ill. 25 cm txt rdacontent n rdamedia nc rdacarrier Includes bibliographical references (p. 395-400) and index Filter <Mathematik> Filters (Mathematics) Methode der kleinsten Quadrate System identification Filterung <Stochastik> swd Kleinste-kwadratenmethode gtt Lineaire algebra gtt Methode der kleinsten Quadrate swd Systeemtheorie gtt Systemidentifikation swd Systemidentifikation (DE-588)4121753-6 gnd rswk-swf Filter Mathematik (DE-588)4154388-9 gnd rswk-swf Methode der kleinsten Quadrate (DE-588)4038974-1 gnd rswk-swf Filter Mathematik (DE-588)4154388-9 s Systemidentifikation (DE-588)4121753-6 s Methode der kleinsten Quadrate (DE-588)4038974-1 s DE-604 Verdult, Vincent Verfasser aut http://www.loc.gov/catdir/enhancements/fy0803/2007298771-b.html Contributor biographical information http://www.loc.gov/catdir/enhancements/fy0803/2007298771-d.html Publisher description http://www.loc.gov/catdir/enhancements/fy0803/2007298771-t.html Table of contents only http://www.loc.gov/catdir/toc/fy0801/2007298771.html Table of contents HEBIS Datenaustausch Darmstadt application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016582328&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Verhaegen, Michel Verdult, Vincent Filtering and system identification a least squares approach Filter <Mathematik> Filters (Mathematics) Methode der kleinsten Quadrate System identification Filterung <Stochastik> swd Kleinste-kwadratenmethode gtt Lineaire algebra gtt Methode der kleinsten Quadrate swd Systeemtheorie gtt Systemidentifikation swd Systemidentifikation (DE-588)4121753-6 gnd Filter Mathematik (DE-588)4154388-9 gnd Methode der kleinsten Quadrate (DE-588)4038974-1 gnd |
| subject_GND | (DE-588)4121753-6 (DE-588)4154388-9 (DE-588)4038974-1 |
| title | Filtering and system identification a least squares approach |
| title_auth | Filtering and system identification a least squares approach |
| title_exact_search | Filtering and system identification a least squares approach |
| title_exact_search_txtP | Filtering and system identification a least squares approach |
| title_full | Filtering and system identification a least squares approach Michel Verhaegen, Vincent Verdult |
| title_fullStr | Filtering and system identification a least squares approach Michel Verhaegen, Vincent Verdult |
| title_full_unstemmed | Filtering and system identification a least squares approach Michel Verhaegen, Vincent Verdult |
| title_short | Filtering and system identification |
| title_sort | filtering and system identification a least squares approach |
| title_sub | a least squares approach |
| topic | Filter <Mathematik> Filters (Mathematics) Methode der kleinsten Quadrate System identification Filterung <Stochastik> swd Kleinste-kwadratenmethode gtt Lineaire algebra gtt Methode der kleinsten Quadrate swd Systeemtheorie gtt Systemidentifikation swd Systemidentifikation (DE-588)4121753-6 gnd Filter Mathematik (DE-588)4154388-9 gnd Methode der kleinsten Quadrate (DE-588)4038974-1 gnd |
| topic_facet | Filter <Mathematik> Filters (Mathematics) Methode der kleinsten Quadrate System identification Filterung <Stochastik> Kleinste-kwadratenmethode Lineaire algebra Systeemtheorie Systemidentifikation Filter Mathematik |
| url | http://www.loc.gov/catdir/enhancements/fy0803/2007298771-b.html http://www.loc.gov/catdir/enhancements/fy0803/2007298771-d.html http://www.loc.gov/catdir/enhancements/fy0803/2007298771-t.html http://www.loc.gov/catdir/toc/fy0801/2007298771.html http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016582328&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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