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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.
Artificial neural networks possess several properties that make
them particularly attractive for applications to modelling and
control of complex non-linear systems. Among these properties are
their universal approximation ability, their parallel network
structure and the availability of on- and off-line learning methods
for the interconnection weights. However, dynamic models that
contain neural network architectures might be highly non-linear and
difficult to analyse as a result. Artificial Neural Networks for
Modelling and Control of Non-Linear Systems investigates the
subject from a system theoretical point of view. However the
mathematical theory that is required from the reader is limited to
matrix calculus, basic analysis, differential equations and basic
linear system theory. No preliminary knowledge of neural networks
is explicitly required. The book presents both classical and novel
network architectures and learning algorithms for modelling and
control. Topics include non-linear system identification, neural
optimal control, top-down model based neural control design and
stability analysis of neural control systems. A major contribution
of this book is to introduce NLq Theory as an extension towards
modern control theory, in order to analyze and synthesize
non-linear systems that contain linear together with static
non-linear operators that satisfy a sector condition: neural state
space control systems are an example. Moreover, it turns out that
NLq Theory is unifying with respect to many problems arising in
neural networks, systems and control. Examples show that complex
non-linear systems can be modelled and controlled within NLq
theory, including mastering chaos. The didactic flavor of this book
makes it suitable for use as a text for a course on Neural
Networks. In addition, researchers and designers will find many
important new techniques, in particular NLq Theory, that have
applications in control theory, system theory, circuit theory and
Time Series Analysis.
In recent years, there has been a great interest in large-scale and
real-time matrix compu- tationsj these computations arise in a
variety of fields, such as computer graphics, imaging, speech and
image processing, telecommunication, biomedical signal processing,
optimiza- tion and so on. This volume gives an account of recent
research advances in numerical techniques used in large-scale and
real-time computations and their implementation on high performance
computers. For anyone interested in any of the aforementioned
disci- plines, this collection of papers is of value and provides
state-of-the-art expositions as weil as new and important trends
and directions for the future, motivated and illustrated by a
wealth of scientific and engineering applications. The volume is an
outgrowth of the NATO Advanced Study Institute "Linear Algebra for
Large-Scale and Real-Time Applications," held at Leuven, Belgium,
August 1992. We were quite fortunate to be able to gather such an
exceilent group of researchers to participate in this Institute. We
are indebted to all the participants who enriched the meeting
through their many contributions. Special thanks are due to the
invited speakers at the Institute, P. Bjlifrstad, Universitetet i
Bergen -Norway, S. Boyd, Stanford University -U. S. A. , G. Cy-
benko, Dartmouth College -U. S. A. , J. Demmel, University of
California Berkeley -U. S. A, E. Deprettere, Technische
Universiteit Delft -The Netherlands, P. Dewilde, Technische Uni-
versiteit Delft -The Netherlands, R. Freund, AT & T -U. S. A. ,
M.
Artificial neural networks possess several properties that make
them particularly attractive for applications to modelling and
control of complex non-linear systems. Among these properties are
their universal approximation ability, their parallel network
structure and the availability of on- and off-line learning methods
for the interconnection weights. However, dynamic models that
contain neural network architectures might be highly non-linear and
difficult to analyse as a result. Artificial Neural Networks for
Modelling and Control of Non-Linear Systems investigates the
subject from a system theoretical point of view. However the
mathematical theory that is required from the reader is limited to
matrix calculus, basic analysis, differential equations and basic
linear system theory. No preliminary knowledge of neural networks
is explicitly required. The book presents both classical and novel
network architectures and learning algorithms for modelling and
control. Topics include non-linear system identification, neural
optimal control, top-down model based neural control design and
stability analysis of neural control systems. A major contribution
of this book is to introduce NLq Theory as an extension towards
modern control theory, in order to analyze and synthesize
non-linear systems that contain linear together with static
non-linear operators that satisfy a sector condition: neural state
space control systems are an example. Moreover, it turns out that
NLq Theory is unifying with respect to many problems arising in
neural networks, systems and control. Examples show that complex
non-linear systems can be modelled and controlled within NLq
theory, including mastering chaos. The didactic flavor of this book
makes it suitable for use as a text for a course on Neural
Networks. In addition, researchers and designers will find many
important new techniques, in particular NLq Theory, that have
applications in control theory, system theory, circuit theory and
Time Series Analysis.
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