This monograph opens up new horizons for engineers and researchers in
academia and in industry dealing with or interested in new developments in the
field of system identification and control. It emphasizes guidelines for
working solutions and practical advice for their implementation rather than the
theoretical background of Gaussian process (GP) models. The book demonstrates
the potential of this recent development in probabilistic machine-learning
methods and gives the reader an intuitive understanding of the topic. The
current state of the art is treated along with possible future directions for
research.Systems control design relies on mathematical models and these may be
developed from measurement data. This process of system identification, when
based on GP models, can play an integral part of control design in data-based
control and its description as such is an essential aspect of the text. The
background of GP regression is introduced first with system identification and
incorporation of prior knowledge then leading into full-blown control. The book
is illustrated by extensive use of examples, line drawings, and graphical
presentation of computer-simulation results and plant measurements. The
research results presented are applied in real-life case studies drawn from
successful applications including:a gas–liquid separator
control;urban-traffic signal
modelling and reconstruction; andprediction of atmospheric
ozone concentration.A MATLAB® toolbox, for identification and simulation of
dynamic GP models is provided for download.
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This process of system identification, whenbased on GP models, can play an integral part of control design in data-basedcontrol and its description as such is an essential aspect of the text.
System Identification with GP Models.- Incorporation of Prior Knowledge.- Control with GP Models.- Trends, Challenges and Research Opportunities.- Case Studies.
This monograph opens up
new horizons for engineers and researchers in academia and in industry dealing
with or interested in new developments in the field of system identification
and control. It emphasizes guidelines for working solutions and practical
advice for their implementation rather than the theoretical background of
Gaussian process (GP) models. The book demonstrates the potential of this
recent development in probabilistic machine-learning methods and gives the
reader an intuitive understanding of the topic. The current state of the art is
treated along with possible future directions for research.Systems control design
relies on mathematical models and these may be developed from measurement data.
This process of system identification, when based on GP models, can play an
integral part of control design in data-based control and its description as
such is an essential aspect of the text. The background of GP regression is
introduced first with system identification and incorporation of prior
knowledge then leading into full-blown control. The book is illustrated by
extensive use of examples, line drawings, and graphical presentation of
computer-simulation results and plant measurements. The research results
presented are applied in real-life case studies drawn from successful
applications including:a gas–liquid separator control;urban-traffic signal modelling and reconstruction; andprediction of atmospheric ozone concentration.A MATLAB® toolbox,
for identification and simulation of dynamic GP models is provided for
download.Advances in Industrial
Control aims to report and
encourage the transfer of technology in control engineering. The rapid
development of control technology has an impact on all areas of the control
discipline. The series offers an opportunity for researchers to present an
extended exposition of new work in all aspects of industrial control.
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Explains how theoretical work in Gaussian process models can be applied in the control of real industrial systems Provides the engineer with practical guidance is not unduly encumbered by complicated theory Shows the academic researcher the potential for real-world application of a recent branch of control theory Includes supplementary material: sn.pub/extras
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Produktdetaljer
ISBN
9783319210209
Publisert
2015-12-07
Utgiver
Vendor
Springer International Publishing AG
Høyde
235 mm
Bredde
155 mm
Aldersnivå
Research, P, 06
Språk
Product language
Engelsk
Format
Product format
Innbundet
Forfatter