Subspace Methods for System Identification
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Beschreibung
System identification provides methods for the sensible approximation of real systems using a model set based on experimental input and output data. Tohru Katayama sets out an in-depth introduction to subspace methods for system identification in discrete-time linear systems thoroughly augmented with advanced and novel results. The text is structured into three parts.
First, the mathematical preliminaries are dealt with: numerical linear algebra; system theory; stochastic processes; and Kalman filtering. The second part explains realization theory, particularly that based on the decomposition of Hankel matrices, as it is applied to subspace identification methods. Two stochastic realization results are included, one based on spectral factorization and Riccati equations, the other on canonical correlation analysis (CCA) for stationary processes. Part III uses the development of stochastic realization results, in the presence of exogenous inputs, to demonstrate the closed-loop application of subspace identification methods CCA and ORT (based on orthogonal decomposition).
The addition of tutorial problems with solutions and Matlab® programs which demonstrate various aspects of the methods propounded to introductory and research material makes Subspace Methods for System Identification not only an excellent reference for researchers but also a very useful text for tutors and graduate students involved with courses in control and signal processing. The book can be used for self-study and will be of much interest to the applied scientist or engineer wishing to use advanced methods in modeling and identification of complex systems.
An in-depth introduction to subspace methods for system identification in discrete-time linear systems thoroughly augmented with advanced and novel results, this text is structured into three parts.
Part I deals with the mathematical preliminaries: numerical linear algebra; system theory; stochastic processes; and Kalman filtering. Part II explains realization theory as applied to subspace identification. Stochastic realization results based on spectral factorization and Riccati equations, and on canonical correlation analysis for stationary processes are included. Part III demonstrates the closed-loop application of subspace identification methods.
Subspace Methods for System Identification is an excellent reference for researchers and a useful text for tutors and graduate students involved in control and signal processing courses. It can be used for self-study and will be of interest to applied scientists or engineers wishing to use advanced methods in modeling and identification of complex systems.
The introductory material combined with novel research results and proofs will make this an all-in-one reference of subspace identification methods for control and signal processing researchers Develops new results in stochastic realization theory incorporating the presence of exogenous inputs Chapter-by-chapter examples, exercises and solutions will guide the graduate student through learning basic and abstruse aspects of subspace methods and system identification Tutors and supervisors are provided with ready checks on their students’ progress with exercise solutions and pre-written Matlab® programs reducing preparation effort to a minimum Includes supplementary material: sn.pub/extras
Autor*in
Tohru Katayama
Themen in »Subspace Methods for System Identification«
Control Control Engineering Control Systems Control Theory Filter Kalman Filtering Kalman filter Linear Systems Process Control Realization Theory Subspace development filtering signal processing systems theory
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From the reviews:
"The book is devoted to subspace methods used for system identification. … The book contains also some tutorial problems with solutions and MATLAB programs, which demonstrate various aspects of the methods propounded to introductory and research material. Therefore it may be a valuable reference for researches as well, a very useful text for tutors and graduate students involved with courses in control and signal processing. The book is clearly written and well organized." (Ryszard Gessing, Zentralblatt MATH, Vol. 1118 (20), 2007)
"Subspace identification methods have become a major tool in system identification during the last decades. … The book is written in a systematic way and generally easy to follow. The ideas are presented in a systematic and coherent manner. … the monograph is suited for researchers, practitioners and graduate students, in particular from an (systems) engineering community. It provides an excellent reference book for realization theory and linear systems." (Wolfgang Scherrer, International Journal of Robust and Nonlinear Control, Vol. 18, 2008)
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