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About this book
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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Information
Table of contents
- Contents
- Preface
- Acknowledgments
- List of Operators and Notational Conventions
- List of Symbols
- List of Abbreviations
- CHAPTER 1 An Introduction to Identification
- CHAPTER 2 Measurements of Frequency Response Functions
- CHAPTER 3 Frequency Response Function Measurements in the Presence of Nonlinear Distortions
- CHAPTER 4 Design of Excitation Signals
- CHAPTER 5 Models of Linear Time-Invariant Systems
- CHAPTER 6 An Intuitive Introduction to Frequency Domain Identification
- CHAPTER 7 Estimation with Known Noise Model
- CHAPTER 8 Estimation with Unknown Noise Model
- CHAPTER 9 Model Selection and Validation
- CHAPTER 10 Basic Choices in System Identification
- CHAPTER 11 Guidelines for the User
- CHAPTER 12 Applications
- CHAPTER 13 Some Linear Algebra Fundamentals
- CHAPTER 14 Some Probability and Stochastic Convergence Fundamentals
- CHAPTER 15 Properties of Least Squares Estimators with Deterministic Weighting
- CHAPTER 16 Properties of Least Squares Estimators with Stochastic Weighting
- CHAPTER 17 Identification of Semilinear Models
- CHAPTER 18 Identification of Invariants of (Over)Parameterized Models
- REFERENCES
- SUBJECT INDEX
- REFERENCE INDEX
- ABOUT THE AUTHORS