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Artificial Neural Networks for Modelling and Control of Non-Linear Systems (Paperback, Softcover reprint of hardcover 1st ed. 1996)
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Artificial Neural Networks for Modelling and Control of Non-Linear Systems (Paperback, Softcover reprint of hardcover 1st ed. 1996)
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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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