System Identification: A Frequency Domain Approach

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John Wiley & Sons, 5 apr. 2004 - 648 sidor
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Electrical Engineering System Identification A Frequency Domain Approach How does one model a linear dynamic system from noisy data? This book presents a general approach to this problem, with both practical examples and theoretical discussions that give the reader a sound understanding of the subject and of the pitfalls that might occur on the road from raw data to validated model. The emphasis is on robust methods that can be used with a minimum of user interaction. Readers in many fields of engineering will gain knowledge about:
* Choice of experimental setup and experiment design
* Automatic characterization of disturbing noise
* Generation of a good plant model
* Detection, qualification, and quantification of nonlinear distortions
* Identification of continuous- and discrete-time models
* Improved model validation tools
and from the theoretical side about:
* System identification
* Interrelations between time- and frequency-domain approaches
* Stochastic properties of the estimators
* Stochastic analysis
System Identification: A Frequency Domain Approach is written for practicing engineers and scientists who do not want to delve into mathematical details of proofs. Also, it is written for researchers who wish to learn more about the theoretical aspects of the proofs. Several of the introductory chapters are suitable for undergraduates. Each chapter begins with an abstract and ends with exercises, and examples are given throughout.
 

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CHAPTER 1 An Introduction to Identification
1
CHAPTER 2 Measurements of Frequency Response Functions
33
CHAPTER 3 Frequency Response Function Measurements in the Presence of Nonlinear Distortions
69
CHAPTER 4 Design of Excitation Signals
115
CHAPTER 5 Models of Linear TimeInvariant Systems
139
CHAPTER 6 An Intuitive Introduction to Frequency Domain Identification
177
CHAPTER 7 Estimation with Known Noise Model
183
CHAPTER 8 Estimation with Unknown Noise Model
281
CHAPTER 13 Some Linear Algebra Fundamentals
413
CHAPTER 14 Some Probability and Stochastic Convergence Fundamentals
435
CHAPTER 15 Properties of Least Squares Estimators with Deterministic Weighting
495
CHAPTER 16 Properties of Least Squares Estimators with Stochastic Weighting
521
CHAPTER 17 Identification of Semilinear Models
535
CHAPTER 18 Identification of Invariants of OverParameterized Models
569
REFERENCES
581
SUBJECT INDEX
593

CHAPTER 9 Model Selection and Validation
321
CHAPTER 10 Basic Choices in System Identification
351
CHAPTER 11 Guidelines for the User
377
CHAPTER 12 Applications
393
REFERENCE INDEX
601
ABOUT THE AUTHORS
605
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Sidan i - Piscataway, NJ 08854 IEEE Press Editorial Board Stamatios V. Kartalopoulos, Editor in Chief M. Akay ME El-Hawary M. Padgett JB Anderson RJ Herrick WD Reeve RJ Baker D. Kirk S. Tewksbury JE Brewer R.

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Om författaren (2004)

About the Authors Rik Pintelon and Johan Schoukens are professors of electrical engineering at the Vrije Universiteit Brussels, Brussels, Belgium. They share research interests in system identification, signal processing, and measurement techniques. They are the coauthors of a software package with a user-friendly graphical user interface called Frequency Domain System Identification Toolbox for Matlab(r), which covers the methods discussed in this book.

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