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Subspace Methods for System Identification (Communications and Control Engineering)
 
 
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Subspace Methods for System Identification (Communications and Control Engineering) [Hardcover]

Tohru Katayama (Author)

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Book Description

1852339810 978-1852339814 October 20, 2005 1st Edition.
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.

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Editorial Reviews

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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)

From the Back Cover

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.

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Inside This Book (learn more)
Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
optimal estimation, stochastic realization theory, stochastic balanced realization, stochastic realization problem, deterministic realization theory, forward innovation model, identifying state space models, balanced stochastic realization, stochastic realization algorithm, canonical correlations between the future, extended observability matrix, stochastic subsystem, deterministic subsystem, overlapping parametrization, subspace identification methods, strictly positive real conditions, impulse response matrices, reachability matrices, white noise with mean zero, splitting subspace, state covariance matrix, canonical angles, spectral density matrix, innovation representation, spectral factor
Key Phrases - Capitalized Phrases (CAPs): (learn more)
Stochastic Processes, Discrete-Time Linear Systems, Identification of Closed-loop System, Realization of Deterministic Systems, Akaike's Method, Method Step, Ho-Kalman's Method, Orthogonal Decomposition of Output Process, Let the Cholesky, Problem Formulation, Appendix of Section, Under Assumption
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