Filtering and System Identification

A Least Squares Approach

Inbunden, Engelska, 2007

AvMichel Verhaegen,Vincent Verdult

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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.

Produktinformation

Utgivningsdatum2007-04-26
Mått180 x 254 x 25 mm
Vikt980 g
FormatInbunden
SpråkEngelska
Antal sidor422
FörlagCambridge University Press
ISBN9780521875127

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Michel Verhaegen is professor and co-director of the Delft Center for Systems and Control at the Delft University of Technology in The Netherlands. His current research involves applying new identification and controller design methodologies to industry, with particular focus on areas such as mechatronics and microsystems, physical imaging, and smart transportation systems. Vincent Verdult was an assistant professor in systems and control at the Delft University of Technology in The Netherlands, from 2001 to 2005, where his research focused on system identification for nonlinear state-space systems. He is currently working in the field of information theory.

Preface; 1. Introduction; 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; References; Index.