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Subspace Identification for Linear Systems - Theory - Implementation - Applications (Paperback, Softcover reprint of the original 1st ed. 1996)
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Subspace Identification for Linear Systems - Theory - Implementation - Applications (Paperback, Softcover reprint of the original 1st ed. 1996)
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Total price: R3,733
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Subspace Identification for Linear Systems focuses on the theory,
implementation and applications of subspace identification
algorithms for linear time-invariant finite- dimensional dynamical
systems. These algorithms allow for a fast, straightforward and
accurate determination of linear multivariable models from measured
input-output data. The theory of subspace identification algorithms
is presented in detail. Several chapters are devoted to
deterministic, stochastic and combined deterministic-stochastic
subspace identification algorithms. For each case, the geometric
properties are stated in a main 'subspace' Theorem. Relations to
existing algorithms and literature are explored, as are the
interconnections between different subspace algorithms. The
subspace identification theory is linked to the theory of frequency
weighted model reduction, which leads to new interpretations and
insights. The implementation of subspace identification algorithms
is discussed in terms of the robust and computationally efficient
RQ and singular value decompositions, which are well-established
algorithms from numerical linear algebra. The algorithms are
implemented in combination with a whole set of classical
identification algorithms, processing and validation tools in
Xmath's ISID, a commercially available graphical user interface
toolbox. The basic subspace algorithms in the book are also
implemented in a set of Matlab files accompanying the book. An
application of ISID to an industrial glass tube manufacturing
process is presented in detail, illustrating the power and
user-friendliness of the subspace identification algorithms and of
their implementation in ISID. The identified model allows for an
optimal control of the process, leading to a significant
enhancement of the production quality. The applicability of
subspace identification algorithms in industry is further
illustrated with the application of the Matlab files to ten
practical problems. Since all necessary data and Matlab files are
included, the reader can easily step through these applications,
and thus get more insight in the algorithms. Subspace
Identification for Linear Systems is an important reference for all
researchers in system theory, control theory, signal processing,
automization, mechatronics, chemical, electrical, mechanical and
aeronautical engineering.
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