System identification: theory for the user
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Upper Saddle River, NJ
Prentice-Hall
1999
|
| Ausgabe: | 2. ed. |
| Schriftenreihe: | Prentice Hall information and system sciences series
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Hier auch später erschienene, unveränderte Nachdrucke |
| Beschreibung: | XXII, 609 S. graph. Darst. |
| ISBN: | 0136566952 9780136566953 |
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| 245 | 1 | 0 | |a System identification |b theory for the user |c Lennart Ljung |
| 250 | |a 2. ed. | ||
| 264 | 1 | |a Upper Saddle River, NJ |b Prentice-Hall |c 1999 | |
| 300 | |a XXII, 609 S. |b graph. Darst. | ||
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| 650 | 4 | |a Systemidentifikation | |
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Datensatz im Suchindex
| _version_ | 1804127171589963777 |
|---|---|
| adam_text | Titel: System identification
Autor: Ljung, Lennart
Jahr: 1999
Contents Preface to the First Edition_xiv Acknowledgments_xvi Preface to the Second Edition_xviii Operators and Notational Conventions_xix ? 1 Introduction _1 1.1 Dynamic Systems 1 1.2 Models 6 1.3 An Archetypical Problem—ARX Models and the Linear Least Squares Method 8 1.4 The System Identification Procedure 13 1.5 Organization of the Book 14 1.6 Bibliography 16 part i: systems and models_ ? 2 Time-Invariant Linear Systems 18 2.1 Impulse Responses, Disturbances, and Transfer Functions 18 2.2 Frequency-Domain Expressions 28 2.3 Signal Spectra 33 2.4 Single Realization Behavior and Ergodicity Results (*) 42 2.5 Multivariable Systems (*) 44 2.6 Summary 45 2.7 Bibliography 46 2.8 Problems 47 Appendix 2A: Proof of Theorem 2.2 52 Appendix 2B: Proof of Theorem 2.3 55 Appendix 2C: Covariance Formulas 61 VM
Contents viii ? 3 Simulation and Prediction _________— 3.1 Simulation 63 3.2 Prediction 64 3.3 Observers 72 3.4 Summary 75 3.5 Bibliography 75 3.6 Problems 76 ? 4 Models of Linear Time-Invariant Systems __79 4.1 Linear Models and Sets of Linear Models 79 4.2 A Family of Transfer-Function Models 81 4.3 State-Space Models 93 4.4 Distributed Parameter Models (*) 103 4.5 Model Sets, Model Structures, and Identifiability: Some Formal Aspects (*) 105 4.6 Identifiability of Son 1 res 114 4.7 Summary 118 4.8 Bibliography 119 4.9 Problems 121 Appendix 4A: Identifiability of Black-Box Multivariable Model Structures 128 ? 5 Models for Time-varying and Nonlinear Systems 140 5.1 Linear Time-Varying Models 140 5.2 Models with Nonlinearities 143 5.3 Nonlinear State-Space Models 146 5.4 Nonlinear Black-Box Models: Basic Principles 148 5.5 Nonlinear Black-Box Models: Neural Networks, Wavelets and Classical Models 154 5.6 Fuzzy Models 156 5.7 Formal Characterization of Models (*) 161 5.8 Summary 164 5.9 Bibliography 165 5.10 Problems 165
Contents IX part ii: methods_ ? 6 Nonparametric Time- and Frequency-Domain Methods 168 6.1 Transient-Response Analysis and Correlation Analysis 168 6.2 Frequency-Response Analysis 170 6.3 Fourier Analysis 173 6.4 Spectral Analysis 178 6.5 Estimating the Disturbance Spectrum (*) 187 6.6 Summary 189 6.7 Bibliography 190 6.8 Problems 191 Appendix 6A: Derivation of the Asymptotic Properties of the Spectral Analysis Estimate 194 ? 7 Parameter Estimation Methods 197 7.1 Guiding Principles Behind Parameter Estimation Methods 197 7.2 Minimizing Prediction Errors 199 7.3 Linear Regressions and the Least-Squares Method 203 A Statistical Framework for Parameter Estimation and the Likelihood Method 212 7.5 Correlating Prediction Errors with Past Data 222 7.6 Instrumental-Variable Methods 224 7.7 Using Frequency Domain Data to Fit Linear Models (*) 227 7.8 Summary 233 7.9 Bibliography 234 7.10 Problems 236 Appendix 7A: Proof of the Cramer-Rao Inequality 245 ? 8 Convergence and Consistency 247 8.1 Introduction 247 8.2 Conditions on the Data Set 249 8.3 Prediction-Error Approach 253 8.4 Consistency and Identifiability 258 8.5 Linear Time-Invariant Models: A Frequency-Domain Description of the Limit Model 263 8.6 The Correlation Approach 269 8.7 Summary 273 8.8 Bibliography 274 8.9 Problems 275
X Contents ? 9 Asymptotic Distribution of Parameter Estimates 280 9.1 Introduction 280 9.2 The Prediction-Error Approach: Basic Theorem 281 9.3 Expressions for the Asymptotic Variance 283 9.4 Frequency-Domain Expressions for the Asymptotic Variance 290 9.5 The Correlation Approach 296 9.6 Use and Relevance of Asymptotic Variance Expressions 302 9.7 Summary 304 9.8 Bibliography 305 9.9 Problems 305 Appendix 9A: Proof of Theorem 9.1 309 Appendix 9B: The Asymptotic Parameter Variance 313 ? 10 Computing the Estimate 317 10.1 Linear Regressions and Least Squares 317 10.2 Numerical Solution by Iterative Search Methods 326 10.3 Computing Gradients 329 10.4 Two-Stage and Multistage Methods 333 10.5 Local Solutions and Initial Values 338 10.6 Subspace Methods for Estimating State Space Models 340 10.7 Summary 351 10.8 Bibliography 352 10.9 Problems 353 ? 11 Recursive Estimation Methods _ 361 11.1 Introduction 361 11.2 The Recursive Least-Squares Algorithm 363 11.3 The Recursive IV Method 369 11.4 Recursive Prediction-Error Methods 370 11.5 Recursive Pseudolinear Regressions 374 11.6 The Choice of Updating Step 376 11.7 Implementation 382 11.8 Summary 386 11.9 Bibliography 387 11.10 Problems 388 Appendix 11 A: Techniques for Asymptotic Analysis of Recursive Algorithms 389 11A Problems 398
Contents xi part iii: user s choices ? 12 Options and Objectives 399 12.1 Options 399 12.2 Objectives 400 12.3 Bias and Variance 404 12.4 Summary 406 12.5 Bibliography 406 12.6 Problems 406 ? 13 Experiment Design 408 13.1 Some General Considerations 408 13.2 Informative Experiments 411 13.3 Input Design for Open Loop Experiments 415 13.4 Identification in Closed Loop: Identifiability 428 13.5 Approaches to Closed Loop Identification 434 13.6 Optimal Experiment Design for High-Order Black-Box Models 441 13.7 Choice of Sampling Interval and Presampling Filters 444 13.8 Summary 452 13.9 Bibliography 453 13.10 Problems 454 ? 14 Preprocessing Data 458 14.1 Drifts and Detrending 458 14.2 Outliers and Missing Data 461 14.3 Selecting Segments of Data and Merging Experiments 464 14.4 Prefiltering 466 14.5 Formal Design of Prefiltering and Input Properties 470 14.6 Summary 474 14.7 Bibliography 475 14.8 Problems 475 ? 15 Choice of Identification Criterion _477 15.1 General Aspects 477 15.2 Choice of Norm: Robustness 479 15.3 Variance-Optimal Instruments 485 15.4 Summary 488
Contents Bibliography 489 Problems 490 16 Model Structure Selection and Model Validation _ 491 16.1 General Aspects of the Choice of Model Structure 491 16.2 A Priori Considerations 493 16.3 Model Structure Selection Based on Preliminary Data Analysis 495 16.4 Comparing Model Structures 498 5.5 Model Validation 509 6.6 Residual Analysis 511 16.7 Summary 516 16.8 Bibliography 517 16.9 Problems 518 ? 17 System Identification in Practice 520 17.1 The Tool: Interactive Software 520 17.2 The Practical Side of System Identification 522 17.3 Some Applications 525 17.4 What Does System Identification Have To Offer? 536 ? Appendix I Some Concepts From Probability Theory 539 ? Appendix II Some Statistical Techniques for Linear Regressions 543 II. 1 Linear Regressions and the Least Squares Estimate 543 11.2 Statistical Properties of the Least-Squares Estimate 551 11.3 Some Further Topics in Least-Squares Estimation 559 11.4 Problems 564 References_____565 Subject Index _____ : _596 xii 15.5 15.6 ? Reference Index 603
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| dewey-full | 003.1 629.8312 |
| dewey-hundreds | 000 - Computer science, information, general works 600 - Technology (Applied sciences) |
| dewey-ones | 003 - Systems 629 - Other branches of engineering |
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| dewey-search | 003.1 629.8312 |
| dewey-sort | 13.1 |
| dewey-tens | 000 - Computer science, information, general works 620 - Engineering and allied operations |
| discipline | Informatik Mathematik Mess-/Steuerungs-/Regelungs-/Automatisierungstechnik Mess-/Steuerungs-/Regelungs-/Automatisierungstechnik / Mechatronik |
| edition | 2. ed. |
| format | Book |
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| isbn | 0136566952 9780136566953 |
| language | English |
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| spelling | Ljung, Lennart Verfasser aut System identification theory for the user Lennart Ljung 2. ed. Upper Saddle River, NJ Prentice-Hall 1999 XXII, 609 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Prentice Hall information and system sciences series Hier auch später erschienene, unveränderte Nachdrucke Reglerteori sao Systemidentifikation Systemteori sao System identification Systemidentifikation (DE-588)4121753-6 gnd rswk-swf Systemidentifikation (DE-588)4121753-6 s DE-604 HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=008503997&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Ljung, Lennart System identification theory for the user Reglerteori sao Systemidentifikation Systemteori sao System identification Systemidentifikation (DE-588)4121753-6 gnd |
| subject_GND | (DE-588)4121753-6 |
| title | System identification theory for the user |
| title_auth | System identification theory for the user |
| title_exact_search | System identification theory for the user |
| title_full | System identification theory for the user Lennart Ljung |
| title_fullStr | System identification theory for the user Lennart Ljung |
| title_full_unstemmed | System identification theory for the user Lennart Ljung |
| title_short | System identification |
| title_sort | system identification theory for the user |
| title_sub | theory for the user |
| topic | Reglerteori sao Systemidentifikation Systemteori sao System identification Systemidentifikation (DE-588)4121753-6 gnd |
| topic_facet | Reglerteori Systemidentifikation Systemteori System identification |
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