Filtering and System Identification
A Least Squares Approach
$51.00 ( ) USD
- Michel Verhaegen, Technische Universiteit Delft, The Netherlands
- Vincent Verdult, Technische Universiteit Delft, The Netherlands
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Filtering and system identification are powerful techniques for building models of complex systems. This 2007 book discusses the design of reliable numerical methods to retrieve missing information in models derived using these techniques. Emphasis is on the least squares approach as applied to the linear state-space model, and problems of increasing complexity are analyzed and solved within this framework, starting with the Kalman filter and concluding with the estimation of a full model, noise statistics and state estimator directly from the data. Key background topics, including linear matrix algebra and linear system theory, are covered, followed by different estimation and identification methods in the state-space model. With end-of-chapter exercises, MATLAB simulations and numerous illustrations, this book will appeal to graduate students and researchers in electrical, mechanical and aerospace engineering. It is also useful for practitioners. Additional resources for this title, including solutions for instructors, are available online at www.cambridge.org/9780521875127.Read more
- Uses a least squares approach with emphasis on numerical implementation, making the topic more accessible and reducing the gap between theory and practice
- MATLAB simulations allow hands-on experience in applying the theory
- End-of-chapter homework problems, with solutions for instructors available online, make this a valuable resource for students
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- Date Published: May 2007
- format: Adobe eBook Reader
- isbn: 9780511276514
- availability: This ISBN is for an eBook version which is distributed on our behalf by a third party.
Table of Contents
2. Linear algebra
3. Discrete-time signals and systems
4. Random variables and signals
5. Kalman filtering
6. Estimation of spectra and frequency response functions
7. Output-error parametric model estimation
8. Prediction-error parametric model estimation
9. Subspace model identification
10. The system identification cycle
Notation and symbols
List of abbreviations
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