Description du livre
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; and
prediction of atmospheric
ozone concentration.
A MATLAB® toolbox, for identification and simulation of
dynamic GP models is provided for download.