Pages: 392, Size: 13.2 MB
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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